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This PDF file contains the front matter associated with SPIE
Proceedings Volume 6969, including the Title Page, Copyright
information, Table of Contents, Introduction (if any), and the
Conference Committee listing.
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Effective missile warning and countermeasures remain an unfulfilled goal for the Air Force and others in the DOD community. To make the expectations a reality, newer sensors exhibiting the required sensitivity, field of regard, and spatial resolution are being developed and transitioned. The largest concern is in the first stage of a missile warning system: detection, in which all targets need to be detected with a high confidence and with very few false alarms. Typical fielded sensors are limited in their detection capability by either lack of sensitivity or by the presence of heavy background clutter, sun glints, and inherent sensor noise. Many threat environments include false alarm sources like burning fuels, flares, exploding ordinance, arc welders, and industrial emitters. Multicolor discrimination has been shown as one of the effective ways to improve the performance of missile warning sensors, particularly for heavy clutter situations. Its utility has been demonstrated in multiple demonstration and fielded systems. New exploitations of background and clutter spectral contents, coupled with advanced spatial and temporal filtering techniques, have resulted in a need to have a new baseline algorithm on which future processing advances may be judged against. This paper describes the AFRL Suite IIIc algorithm chain and its performance against long-range dim targets in clutter.
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A Chem/Bio Defense Algorithm Benchmark is proposed as a way to leverage algorithm expertise and apply it to high
fidelity Chem/Bio challenge problems in a high fidelity simulation environment. Initially intended to provide risk
mitigation to the DTRA-sponsored US Army CUGR ACTD, its intent is to enable the assessment and transition of
algorithms to support P3I of future spiral updates. The key chemical sensor in the CUGR ACTD is the Joint
Contaminated Surface Detector (JCSD), a short-range stand-off Raman spectroscopy sensor for tactical in-the-field
applications. The significant challenges in discriminating chemical signatures in such a system include, but are not
limited to, complex background clutter and low signal to noise ratios (SNR). This paper will present an overview of the
Chem-Bio Defense Algorithm Benchmark, and the JCSD Challenge Problem specifically.
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Because of the unique Raman spectrum of a chemical, Raman spectroscopy can be used to identify chemicals
on a surface. In this paper chemical detection and classification in a stationary background are addressed.
Firstly, because the autoregressive (AR) spectrum is capable of representing a wide range of spectra, both the
pure background and background plus a chemical are modeled as AR spectra with different coefficients. Based
on this modeling, a generalized likelihood ratio test (GLRT) is proposed to detect abnormal chemicals in the
background. In essence, the GLRT detector tests if the data can be represented by a known AR background
spectrum. With the AR spectrum modeling, a classifier based on the locally most powerful test is also proposed
to classify the detected chemicals. Computer simulation results are given, which show the effectiveness of the
proposed algorithms. Practical problems, such as setting the detection threshold, extension to nonstationary
backgrounds, and the identifiability of chemicals are also discussed.
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This paper uses super-resolution methods to detect small objects in infrared image sequences from a simulated airborne
platform, using image registration techniques for automatic sightline stabilisation. The scene consists of multiple layers,
corresponding to a static background scene and layers of cloud cover at varying heights. The motivation is to evaluate
the performance of super-resolution methods in the presence of three-dimensional structured infrared clutter.
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Distributed Sensor Concept - DISCO was proposed for multiplication of individual sensor capabilities through non-coherent
cooperative target engagement. The signal processing technique for DISCO is Recursive Adaptive Frame
Integration of Limited data - RAFIL technique that was initially proposed as a way to improve the SNR, reduce
data rate and mitigate FPA noise for IR sensors. In DISCO, the RAFIL technique is used in a segmented way,
when constituencies of the technique are spatially and temporally separated between individual sensors. Each sensor
provides to and receives data from other sensors in the network. In this paper efficiency of DISCO is discussed for
acquisition, accurate handover and track correlation of small targets.
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Hyperspectral images provide scientists and engineers with the capability of precise material identification in
remote sensing applications. One can leverage this data for precise track identification (ID) and incorporate the
high-confidence ID in the tracking process. Our previous work demonstrates that hyperspectral-aided tracking
outperforms kinematic-only tracking where multiple ambiguous situations exist. We develop a novel gating concept
for hyperspectral measurements, similar in concept to the gating of the Mahalanobis distance computed
from the Kalman residuals. Our spectral gating definition is based on the distance between the spectral distribution
of the class ID of a track and the spectral distribution of the class ID resulting from the classification
of a measurement. We further incorporate the distance between each class distribution (in spectral space) in
the track association portion of our hyperspectral-aided tracker. Since functional forms of the joint probability
distribution function do not exist, similarity measures such as the Kullback-Leibler divergence or Bhattacharyya
distance cannot be used. Instead, we compute all pair-wise distances between all samples of the two classes and
then summarize these distances in a meaningful way. This article presents our novel spectral gating approach
and its use in track association. It further explores different similarity measures and their effect on spectral
gating and track association.
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Differentiation between particulate biological agents and non-biological agents is typically performed via a
time-consuming "wet chemistry" process or through the use of fluorescent and spectroscopic analysis.
However, while these methods can provide definitive recognition of biological agents, many of them have to
be performed in a laboratory environment, or are difficult to implement in the field. Optical recognition
techniques offer an additional recognition approach that can provide rapid analysis of a material in-situ to
identify those materials that may be biological in nature. One possible application is to use these techniques
to "screen" suspicious materials and to identify those that are potentially biological in nature. Suspicious
materials identified by this screening process can then be analyzed in greater detail using the other, more
definitive (but time consuming) analysis techniques. This presentation will describe the results of a feasibility
study to determine whether optical pattern recognition techniques can be used to differentiate biological
related materials from non-biological materials. As part of this study, feature extraction algorithms were
developed utilizing multiple contrast and texture based features to characterize the macroscopic properties of
different materials. In addition, several pattern recognition approaches using these features were tested
including cluster analysis and neural networks. Test materials included biological agent simulants, biological
agent related materials, and non-biological materials (suspicious white powders). Results of a series of
feasibility tests will be presented along with a discussion of the potential field applications for these
techniques.
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This paper describes a novel set of algorithms that allows indoor activity to be monitored using data from very low
resolution imagers and other non-intrusive sensors. The objects are not resolved but activity may still be determined.
This allows the use of such technology in sensitive environments where privacy must be maintained. Spectral un-mixing
algorithms from remote sensing were adapted for this environment. These algorithms allow the fractional contributions
from different colours within each pixel to be estimated and this is used to assist in the detection and monitoring of small
objects or sub-pixel motion.
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Operating in a coastal environment, with a multitude of boats of different sizes, detection of small extended targets is
only one problem. A further difficulty is in discriminating detections of possible threats from alarms due to sea and
coastal clutter, and from boats that are neutral for a specific operational task. Adding target features to detections allows
filtering out clutter before tracking. Features can also be used to add labels resulting from a classification step. Both will
help tracking by facilitating association. Labeling and information from features can be an aid to an operator, or can
reduce the number of false alarms for more automatic systems.
In this paper we present work on clutter reduction and classification of small extended targets from infrared and visual
light imagery. Several methods for discriminating between classes of objects were examined, with an emphasis on less
complex techniques, such as rules and decision trees. Similar techniques can be used to discriminate between targets and
clutter, and between different classes of boats. Different features are examined that possibly allow discrimination
between several classes. Data recordings are used, in infrared and visual light, with a range of targets including rhibs,
cabin boats and jet-skis.
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We present a method for detecting a large number of moving targets, such as cars and people, in geographically
referenced video. The problem is difficult, due to the large and variable number of targets which enter and leave the field
of view, and due to imperfect geo-projection and registration. In our method, we assume feature extraction produces a
collection of candidate locations (points in 2D space) for each frame. Some of these locations are real objects, but many
are false alarms. Typical feature extraction might be frame differencing, or target recognition. For each candidate
location, and at each time step, our algorithm outputs a velocity estimate and confidence which can be thresholded to
detect objects with constant velocity. In this paper we derive the algorithm, investigate the free parameters, and compare
its performance to a multi-target tracking algorithm.
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The various asymmetrical threats in the urban environment have driven the need for persistent surveillance
and methods to exploit the data provided by passive sensing platforms. The primary goal is to track vehicles
as they move through the urban environment. The rather large number of ambiguous tracking events requires
incorporation of target features to maintain track purity. This paper will discuss a feature extraction technique
that will be referred to as "feature-aided" tracking to mitigate some of the tracking issues in this environment (e.g.
rotation and illumination invariance, partial occlusion, and
move-stop-move transitions). The feature extraction
method applied is loosely based on the SPIN histogram method of applying a two-dimensional histogram relative
to the center of an object. This paper focuses on applying a simplified version of the intensity-based two-dimensional
histogram and gradient-based two-dimensional histogram introduced by the works of Mikolajczyk
and Schmid, and Lazebnik, Schmid, and Ponce. Instead of applying the matching technique on a still frame
subjected to various image transformations, we will apply this technique to sequential frames of imagery in an
urban environment. This approach is intended to be the first of several steps towards eventually integrating a
feature-aided tracking option as one of multiple sources of measurement association. The preliminary results
show potential signs of success especially with rotation-invariance and move-stop-move transitions; however,
additional efforts are required associated with illumination invariance, partial occlusion and disambiguation of
close proximity objects.
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In this paper, a new condition for the target is proposed to increase the robustness of the facet-based detection method
for zero-mean Gaussian noise. In the proposed algorithm, the pixels detected from the maximum extremum condition are
checked further to discern if they are false maximum points in the proposed scheme. The experimental results show that
the proposed algorithm is much more robust for zero-mean Gaussian noise than the conventional detection method.
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Bias introduced due to noisy point estimates being propagated through deterministic nonlinear mappings is a reoccurring
problem in high-fidelity tracking and classification systems. This paper proves that it is a misconception that such bias
is reduced when computing the expected value of the nonlinear output that follows when treating the input as a random
vector with expectation equal to the provided estimate. Instead, this doubles the bias. An approximately unbiased
estimator and an estimate of its covariance matrix are provided. The estimator can be calculated also in the case where
the Hessian matrices associated with the nonlinear mapping are unavailable.
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In this paper, we present an algorithm for determining a velocity probability distribution prior from low frame
rate aerial video of an urban area, and show how this may be used to aid in the multiple target tracking problem,
as well as to provide a foundation for the automated classification of urban transportation infrastructure. The
algorithm used to develop the prior is based on using a generic interest point detector to find automobile
candidate locations, followed by a series of filters based on scale and motion to reduce the number of false
alarms. The remaining locations are then associated between frame pairs using a simple matching algorithm,
and the corresponding tracks are then used to build up velocity histograms in the areas that are moved through
between the track endpoints. The algorithm is tested on a dataset taken over urban Tucson, AZ. The results
demonstrate that the velocity probability distribution prior can be used to infer a variety of information about
road lane directions, speed limits, etc..., as well as providing a means of describing environmental knowledge
about traffic rules that can be used in tracking.
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In order to accurately identify ground targets from radar observations on distributed airborne sensors, range and range-rate
measurement data must be either processed onboard the aircraft or at a common control station. This paper will
show analysis and results that examine the ability of multiple sensors to provide observability of moving targets.
Extremely accurate states are required to support imaging algorithms used to discriminate military targets from civilian
targets. Accurate imaging of targets of interest requires sub-meter range accuracy as well as precise knowledge of the
target heading which is related to the velocity vector accuracy. The tracking algorithms must provide range accuracy on
the order of meters depending on the target spacing and scenario; the imaging pre-processing algorithms can reduce this
error to sub-meter levels. Stringent requirements on heading accuracy may be obviated by the use of prominent point
tracking.
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An assurance region at level p, AP=p, is
an area in motion space that contains the target with assigned
probability p. It is on the basis of AP=p that an action is
taken or a decision made. Common model-based trackers generate a
synthetic distribution function for the kinematic state of the target.
Unfortunately, this distribution is very coarse, and the resulting
AP=p lack credibility. It is shown that a map-enhanced, multiple
model algorithm reduces the tracking error and leads to a compact
assurance region.
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This paper presents a novel continuous approximation approach to nonlinear/non-Gaussian Bayesian tracking.
A good representation of the probability density and likelihood functions is essential for the effectiveness of
nonlinear filtering algorithms since these functions could be multi-modal. The proposed approach uses B-spline
interpolation to represent the density and likelihood functions and tensor product approaches to extend the
filter to multidimensional case. The filter is applicable under most general circumstances since it does not make
any assumption on the form of the underlying probability density. An advantage of the proposed method is
that it retains accurate density information in a continuous low-order polynomial form and finding the target
probability in any region of the state space is straightforward. Further processing based on probability density
such as finding the higher order moments of the state estimates could also be performed with less computational
power. Simulation results are presented to demonstrate the proposed algorithm.
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Video cameras onboard multiple unmanned aerial vehicles (UAVs) can provide effective and inexpensive tracking
and surveillance functions for ground targets. In our previous work, we quantified the degree of nonlinearity (DoN) of
the video filtering problem by considering the perspective transformation for the video measurement model and
constant velocity motion for the target dynamic model. In this paper, we generalize the formulation by using a more
realistic video measurement model which is based on the perspective transformation, radial and tangential lens
distortions, scale, offset, and skew. The centroid pixel coordinates of a target in the digital image represent the sensor
measurement for this model. This measurement model is commonly used in photogrammetry, computer vision, and
video tracking, where significant height variation can occur.
Since the measurement model is a nonlinear function of the target state, the filtering problem is nonlinear. We
quantify the DoN of the video filtering problem by calculating the differential geometry based parameter-effects
curvature and intrinsic curvature. These measures help a filter designer to select an appropriate nonlinear filtering
algorithm for the video filtering problem so that tracking accuracy and computational load requirements are satisfied.
Our results show that the DoN of the video filtering problem is quite low and hence a computationally simple filter such
as the extended Kalman filter (EKF) is a better choice than the particle filter (PF) which has a much higher
computational cost. The state estimation accuracies of the EKF and PF are nearly the same.
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In this paper, both Pareto game theory and learning theory algorithms are utilized in a resource management module for
a practical missile interception system. The resource management module will determine how many and which
antimissiles will be launched for interception. Such interception decisions are based on the number of invading missiles,
availability of antimissiles, special capability of antimissiles, and realistic constraints on the movements of both invading
missiles and antimissiles such as minimum turning radius, maximum velocity, fuel range, etc. Simulations demonstrate
performance improvements when compared to existing strategies (i.e. random assignment), independent of guidance
laws (i.e. Proportional Navigation (PN) or the Differential-Game-based Guidance Law (DGL) guidance laws) under end-game
interception cases or midcourse interception situations.
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In this paper, we consider the tracking of multiple targets in the presence of clutter with poorly localized sensors in
multistatic sensor networks. In multistatic sensor networks, we have a few active sensors that emit the signals and
many passive sensors that receive the signals originated from the active sensors and reflected by the targets and
clutter. In anti-submarine warfare, sensors are typically deployed from aircraft. Optimal tracking performance
can be achieved if all the sensor locations are known. However, in general, sensor deployment accuracy is poor,
and sensors can also drift significantly over time. Hence, the location uncertainties will increase with time. If
the sensors have global position system (GPS) receiver, then their locations can be located with reasonable
accuracy. However, most of the cheap sensors do not have a GPS, and therefor, location uncertainties must
be taken in to consideration while tracking. An advantage of multistatic sensors compared to independent
monostatic sensors is that the sensors can also be tracked accurately. In this paper, we propose how to improve
the tracking performance of multiple targets by incorporating sensor uncertainties. We obtain a bound on the
tracking performance with location uncertainties being taken into consideration, and propose a technique to
select a subset of sensors (if only a few of the available sensors can be used at any measurement time) that
should be used at each time step based on the bound. Simulation results illustrating the performance of the
proposed algorithms are also presented.
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A passive coherent location (PCL) system exploits the ambient FM radio or television signals from powerful
local transmitters, which makes it ideal for covert tracking. In a passive radar system, also known as PCL
system, a variety of measurements can be used to estimate target states such as direction of arrival (DOA), time
difference of arrival (TDOA) or Doppler shift. Noise and the precision of DOA estimation are main issues in
a PCL system and methods such as conventional beam forming (CBF) algorithm, algebraic constant modulus
algorithm (ACMA) are widely analyzed in literature to address them. In practical systems, although it is
necessary to reduce the directional ambiguities, the placement of receivers closed to each other results in larger
bias in the estimation of DOA of signals, especially when the targets move off bore-sight. This phenomenon leads
to degradation in the performance of the tracking algorithm. In this paper, we present a method for removing
the bias in DOA to alleviate the aforementioned problem. The simulation results are presented to show the
effectiveness of the proposed algorithm with an example of tracking airborne targets.
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Conventional algorithms for track association (termed "correlation" by convention) employ algorithms which are
applied to all sensor tracks at a specific time. The overall value of sensor networks for data fusion is closely
tied to the reliability of correct association of common objects tracked by the sensors. Multisensor architectures
consisting of gaps in target coverage requires that tracks must be propagated substantially forward or backward
to a common time for correlation. This naturally gives rise to the question: at which time should track correlation
be performed? In the conventional approach, a two-sensor correlation problem would be solved by propagating
the first sensor's tracks forward to the update time (current time) of the tracks from the second sensor. We
question this approach by showing simulation results that indicate that the current time can be the worst time
to correlate. In addition, a methodology for calculating the approximate optimal correlation time for linear-Gaussian tracking problems is provided.
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A framework of simultaneously estimating the motion and structure parameters of a 3D object by using high
range resolution (HRR) and ground moving target indicator (GMTI) measurements with template information
is given. By decoupling the motion and structure information and employing rigid-body constraints, we have
developed the kinematic and measurement equations of the problem. Since the kinematic system is unobservable
by using only one scan HRR and GMTI measurements, we designed an architecture to run the motion and
structure filters in parallel by using multi-scan measurements. Moreover, to improve the estimation accuracy
in large noise and/or false alarm environments, an interacting multi-template joint tracking (IMTJT) algorithm
is proposed. Simulation results have shown that the averaged root mean square errors for both motion and
structure state vectors have been significantly reduced by using the template information.
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Due to the availability of cheap passive sensors, it is possible to deploy a large number of them for tracking
purposes in anti-submarine warfare (ASW). However, modern submarines are quiet and difficult to track with
passive sensors alone. Multistatic sensor networks, which have few transmitters (e.g., dipping sonars) in addition
to passive receivers, have the potential to improve the tracking performance. We can improve the performance
further by moving the transmitters according to existing target states and any possible new targets. Even
though a large number of passive sensors are available, due to frequency, processing power and other physical
limitations, only a few of them can be used at any one time. Then the problems are to decide the path of the
transmitters and select a subset from the available passive sensors in order to optimize tracking performance.
In this paper, the PCRLB, which gives a lower bound on estimation uncertainty, is used as the performance
measure. We present an algorithm to decide jointly the optimal path of the movable transmitters, by considering
their operational constraints, and the optimal subset of passive sensors that should be used at each time steps for
tracking multiple, possibly time-varying, number of targets. Finding the optimal solution in real time is difficult
for large scale problems, and we propose a genetic algorithm based suboptimal solution technique. Simulation
results illustrating the performance of the proposed algorithm are also presented.
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We consider target detection and tracking of stealthy targets. These targets can be characterized by a strong
aspect dependence leading to difficult detectability without a
multi-static setup. Even in a multi-static setup only
sensors in a certain zone can detect the return signal, if the the aspect dependent return has a small bandwidth.
We propose a solution based on a large number of simple sensor, as using many receivers increases the probability
of detection. The sensors are simple in the sense that they only transmit binary detection results to a fusion
center that has comparatively deep capabilities, and they do not need to know their own position or communicate
with other sensors. We characterize the target position estimation performance using the Cramer-Rao bound
and simulation results, considering uncertainty in nuisance parameters as the sensor positions or the specifics
of the aspect dependence. We suggest a data collection protocol that includes locating sensors that detect the
target and has low communication complexity. As a novelty we also include information about "non-localized"
sensors, as sensors which do not detect the target stay quiet to save bandwidth and energy, therefore are not
known to the fusion center except via knowledge of the deployed sensor density and deployment region.
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The central problem in multitarget, multisensor surveillance is that of determining which reports from separate sensors arise
from common objects. Due to stochastic errors in the source reports, there may be multiple data association hypotheses
with similar likelihoods. Moreover, established methods for performing data association make fundamental modeling
assumptions that hold only approximately in practice. For these reasons, it is beneficial to include some measure of
uncertainty, or ambiguity, when reporting association decisions. In this paper, we perform an analysis of the benefits
versus runtime performance of three methods of producing ambiguity estimates for data association: enumeration of the
k-best data association hypotheses, importance sampling, and Markov Chain Monte Carlo estimation. In addition, we
briefly examine the sensitivity of ambiguity estimates to violations of the stochastic model used in the data association
procedure.
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When compared to tracking airborne targets, tracking ground targets on urban terrains brings a new set of challenges.
Target mobility is constrained by road networks, and the quality of measurements is affected by dense
clutter, multipath, and limited line-of-sight. We investigate the integration of detection, signal processing, tracking,
and scheduling by exploiting distinct levels of diversity: (1) spatial diversity through the use of coordinated
multistatic radars; (2) waveform diversity by adaptively scheduling the transmitted radar waveform according
to the scene conditions; and (3) motion model diversity by using a bank of parallel filters, each one matched to a
different maneuvering model. Specifically, at each scan, the waveform that yields the minimum one-step-ahead
error covariance matrix determinant is transmitted; the received signal is then matched-filtered, and quadratic
curve fitting is applied to extract range and azimuth measurements that are input to the LMIPDA-VSIMM
algorithm for data association and filtering. Monte Carlo simulations are used to demonstrate the effectiveness
of the proposed system on a realistic urban scenario. A more traditional open-loop system, in which waveforms
are scheduled on a round-robin fashion and with no other modes of diversity available, is used as a baseline for
comparison. Simulation results show that our closed-loop system significantly outperforms the baseline system,
presenting both a reduction on the number of lost tracks, and a reduction on the volume of the estimation
uncertainty ellipse. The interdisciplinary nature of this work highlights the challenges involved in designing a
closed-loop active sensing platform for next-generation urban tracking systems.
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Surveillance and ground target tracking using multiple electro-optical and infrared video sensors onboard
unmanned aerial vehicles (UAVs) has drawn a great deal of interest in recent years. We compare a number of track-to-track
fusion algorithms using a single target with the nearly constant velocity dynamic model and two UAVs. A local
tracker is associated with each UAV and processes video measurements to produce local tracks. The video measurement
is the centroid pixel location in the digital image corresponding to the target positions on the ground. In order to handle
arbitrary height variations, we use the perspective transformation for the video measurement model. In addition, the
video measurement model also includes radial and tangential lens distortions, scale, and offset. Since the video
measurement model is a nonlinear function of the target position, the tracking filter uses a nonlinear filtering algorithm.
A fusion center fuses track data received from two local trackers. The track-to-track fusion algorithms employed by the
fusion center include the simple convex combination fusion, Bhattacharya fusion, Bar-Shalom-Campo fusion, and
extended information filter based fusion algorithms. We compare the fusion accuracy, covariance consistency, bias in
the fused estimate, communication load requirements, and scalability. Numerical results are presented using simulated
data.
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With current processing power, Multiple Hypothesis Tracking (MHT) becomes a feasible and powerful solution;
however a good hypothesis pruning method is mandatory for efficient implementation. The availability of a continuously
increasing number of tracking systems raises interest in combining information from these systems. The purpose of this
paper is to propose a method of information fusion for such trackers that use MHT locally with local information sent in
the form of sensor global hypotheses and the fusion center combining them into fused global hypotheses. The
information extracted from the best fused global hypotheses, in the form of ranking of received sensor global
hypotheses, is sent back to local trackers, for optimized pruning. Details of the method, in terms of sensor global
hypotheses generation, evaluation, pruning at local sensors, association and fusion of sensor global hypotheses at fusion
center, and usage of the information received as feedback from the fusion center are presented.
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Multi-sensor tracking holds the potential for improving the surveillance performance achieved through single-sensor
tracking. This potential has been demonstrated in many domains: at NURC, in the context of multi-static
undersea surveillance. Nonetheless, the issue remains of how best to process data in large sensor networks. This
issue is taken up in this paper. We are interested to compare multi-sensor scan-based tracking with a two-stage
approach: static fusion followed by scan-based tracking. This paper focuses on some candidate methodologies
for static fusion. The methods developed in this paper fall into two categories. The scan-based approach
leverages the Gaussian mixture probabilistic hypothesis density (GM-PHD) filter; the batch approaches are
based on scan statistics, and on the multi-hypothesis PDA (MHPDA). Preliminary simulation-based
performance analysis suggests that the MHPDA approach to static fusion is the most robust in dealing with
closely spaced targets and small sensor networks. Leveraging the results presented here, follow-on work will
address the determination of an optimal fusion and tracking architecture. In particular, we will test scan-based
tracking based on the NURC distributed multi-hypothesis tracker (DMHT), with MHPDA processing followed
by scan-based tracking (with the DMHT). We anticipate that, for large sensor networks, the latter approach will
outperform the former.
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Track-to-track fusion (T2TF) is very important in distributed tracking systems. When tracks of a target at
different sensors are fused for increased accuracy, an important issue is to account for the crosscorrelations
among the tracks. In this paper, an exact solution for the general problem of T2TF is proposed. It can be used
with various information structures, e.g., memoryless T2TF or sequential T2TF with information feedback at
arbitrary times. Simulation results for a 1-D tracking scenario evaluate the benefit of the various configurations for
T2TF. It is also observed that T2TF, although done optimally, can be suboptimal w.r.t. centralized measurement
fusion. This is because the locally optimal filter gains are, in general, globally suboptimal. Furthermore, it is
shown that feedback can lead to degradation of the accuracy of the (optimally) fused tracks. Based on the exact
T2TF algorithms, an approximate implementation which requires less communications between the fusion center
and the local trackers is also proposed. This allows the algorithms to be implemented in distributed tracking
systems with low communication capacity. Examples of tracking in two dimensions with two radars, show that
the proposed T2TF algorithms are consistent and can provide significant improvement in accuracy over unfused
tracks. For the sensors-target geometry considered, the T2TF algorithm can even meet the performance bound
of the centralized measurement fusion at the fusion times.
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The primary components of a target track are the estimated state vector and its error variance-covariance matrix (or
simply the covariance). The estimated state indicates the location and motion of the target. The track covariance is
intended to indicate the uncertainty or inaccuracy of the target state estimate. The covariance is computed by the track
processor and may or may not realistically indicate the inaccuracy of the state estimate. Covariance Consistency is the
property that a computed variance-covariance matrix realistically represents the covariance of the actual errors of the
estimate. The computed covariance of the state estimation error is used in the computations of the data association
processing function and the estimation filter; consequently, degraded track consistency might cause misassociations
(correlation errors) and degraded filter processing that can degrade track performance. The computed covariance of the
state estimation error is also used by downstream functions, such as the network-level resource management functions, to
indicate the accuracy of the target state estimate. Hence, degraded track consistency can mislead those functions and the
war fighter about accuracy of each target track.
In the development of target trackers, far more attention has been given to improving the accuracy of the estimated target
state than in improving the track covariance consistency. This paper addresses covariance compensation to reduce the
degradation of consistence due to potential misassociations in measurement fusion using single-frame data association.
This compensation approach used is also applicable to other fusion approaches and to tracking with data from a single
sensor. This paper also shows how this compensation approach can be applied to a variety of data association
algorithms.
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A common problem in video-based tracking of urban targets is occlusion due to buildings and vehicles. Fortunately,
when multiple video sensors are present with enough geometric diversity, track breaks due to temporary
occlusion can be substantially reduced by correlating and fusing source-level track data into system-level tracks.
Furthermore, when operating in a communication-constrained environment, it is preferable to transmit track
data rather than either raw video data or detection measurements. To avoid statistical correlation due to
common prior information, tracklets can be formed from the source tracks prior to transmission to a central
command node, which is then responsible for system track maintenance via correlation and fusion. To maximize
the operational benefit of the system-level track picture, it should be distributed in an efficient manner to all
platforms, especially the local trackers at the sensors. In this paper, we describe a centralized architecture for
multi-sensor video tracking that uses tracklet-based feedback to maintain an accurate and complete track picture
at all platforms. We will also use challenging synthetic video data to demonstrate that our architecture improves
track completeness, enhances track continuity (in the presence of occlusions), and reduces track initiation time
at the local trackers.
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The joint target tracking and classification using target-to-sensor aspect-dependent Radar Cross Section (RCS)
and kinematic data for multistatic sonar network is presented in this paper. The scattered signals measured from
different orientations of a target may vary due to aspect-dependant RCS. A complex target may contain several
dozen significant scattering centers and dozens of other less significant scatterers. Because of this multiplicity
of scatterers, the net RCS pattern exhibits high variation with aspect angle. Thus, radar cross sections from
multiple aspects of a target, which are obtained via multiple sensors, will help in accurately determining the target
class. By modeling the deterministic relationship that exits between RCS and target aspect, both the target class
information and the target orientation can be estimated. Kinematic data are also very helpful in determining the
target class as it describes the target motion pattern and its orientation. The proposed algorithm exploits the
inter-dependency of target state and the target class using aspect-dependent RCS and kinematic information in
order to improve both the state estimates and classification of each target. The simulation studies demonstrate
the merits of the proposed joint target tracking and classification algorithm based on aspect-dependant RCS and
kinematic information.
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This research investigates the impact of scene context knowledge on tracking vehicles in an urban environment
based on video image change detection. The scene context consists of knowledge of the road network and
3D building properties. Airborne sensor position information relative to a 3D model of the context enables
calculation of building occlusions of ground locations. From this context, probability of detection maps that
include regions of interest and smoothed lines-of-sight are developed that assist the change detection algorithm
in reducing false alarms.
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In distributed tracking systems, multiple non-collocated trackers cooperate to fuse local sensor data into a
global track picture. Generating this global track picture at a central location is fairly straightforward, but the
single point of failure and excessive bandwidth requirements introduced by centralized processing motivate the
development of decentralized methods. In many decentralized tracking systems, trackers communicate with their
peers via a lossy, bandwidth-limited network in which dropped, delayed, and out of order packets are typical.
Oftentimes the decentralized tracking problem is viewed as a local tracking problem with a networking twist;
we believe this view can underestimate the network complexities to be overcome. Indeed, a subsequent 'oversight'
layer is often introduced to detect and handle track inconsistencies arising from a lack of robustness to network
conditions.
We instead pose the decentralized tracking problem as a distributed database problem, enabling us to draw
inspiration from the vast extant literature on distributed databases. Using the two-phase commit algorithm, a
well known technique for resolving transactions across a lossy network, we describe several ways in which one
may build a distributed multiple hypothesis tracking system from the ground up to be robust to typical network
intricacies. We pay particular attention to the dissimilar challenges presented by network track initiation vs.
maintenance and suggest a hybrid system that balances speed and robustness by utilizing two-phase commit for
only track initiation transactions. Finally, we present simulation results contrasting the performance of such a
system with that of more traditional decentralized tracking implementations.
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The primary components of a target track are the estimated state vector and its error variance-covariance matrix (or
simply the covariance). The estimated state indicates the location and motion of the target. The track covariance is
intended to indicate the uncertainty or inaccuracy of the target state estimate. The covariance is computed by the track
processor and may or may not realistically indicate the inaccuracy of the state estimate. Covariance Consistency is the
property that a computed variance-covariance matrix realistically represents the covariance of the actual errors of the
estimate. The computed covariance of the state estimation error is used in the computations of the data association
processing function and the estimation filter; consequently, degraded track consistency might cause misassociations
(correlation errors) and degraded filter processing that can degrade track performance. The computed covariance of the
state estimation error is also used by downstream functions, such as the network-level resource management functions, to
indicate the accuracy of the target state estimate. Hence, degraded track consistency can mislead those functions and the
war fighter about accuracy of each target track.
In the development of target trackers, far more attention has been given to improving the accuracy of the estimated target
state than in improving the track covariance consistency. This paper addresses covariance compensation to reduce the
degradation of consistence due to potential misassociations in measurement fusion using single-frame data association.
This compensation approach used is also applicable to other fusion approaches and to tracking with data from a single
sensor. This paper presents simplifications in some of the processing of the covariance compensation to reduce the
processing complexity, i.e. processor load.
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In this paper, we present an efficient data association algorithm for tracking ground targets that perform move-stop-move maneuvers using ground moving target indicator (GMTI) radar. A GMTI radar does not detect the
targets whose radial velocity falls below a certain minimum detectable velocity. Hence, to avoid detection enemy
targets deliberately stop for some time before moving again. When targets perform move-stop-move maneuvers,
a missed detection of a target by the radar leads to an ambiguity as to whether it is because the target has
stopped or due to the probability of detection being less than one. A solution to track move-stop-move target
tracking is based on the variable structure interacting multiple model (VS-IMM) estimator in an ideal scenario
(single target tracking with no false measurements) has been proposed. This solution did not consider the data
association problem. Another solution, called two-dummy solution, considered the data association explicitly and
proposed a solution based on the multiframe assignment algorithm. This solution is computationally expensive,
especially when the scenario is complex (e.g., high target density) or when one wants to perform high dimensional
assignment. In this paper, we propose an efficient multiframe assignment-based solution that considers the second
dummy measurement as a real measurement than a dummy. The proposed algorithm builds a less complex
assignment hypothesis tree, and, as a result, is more efficient in terms of computational resource requirement.
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We describe a new nonlinear filter that is vastly superior to the classic particle filter.
In particular, the computational complexity of the new filter is many orders of
magnitude less than the classic particle filter with optimal estimation accuracy for
problems with dimension greater than 2 or 3. We consider nonlinear estimation
problems with dimensions varying from 1 to 20 that are smooth and fully coupled (i.e.
dense not sparse). The new filter implements Bayes' rule using particle flow rather
than with a pointwise multiplication of two functions; this avoids one of the
fundamental and well known problems in particle filters, namely "particle collapse" as
a result of Bayes' rule. We use a log-homotopy to derive the ODE that describes
particle flow. This paper was written for normal engineers, who do not have
homotopy for breakfast.
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Timely recognition of threats can be significantly supported by security assistance systems that work continuously
in time and call the attention of the security personnel in case of anomalies. We describe the concept and
the realization of an indoor security assistance system for real-time decision support. Data for the classification
of persons are provided by chemical sensors detecting hazardous materials. Due to their limited spatio-temporal
resolution, a single chemical sensor cannot localize this material and associate it with a person. We compensate
this deficiency by fusing the output of multiple, distributed chemical sensors with kinematical data from
laser-range-scanners. Both, tracking and fusion of tracks with chemical attributes can be processed within one
single framework called Probabilistic Multiple Hypothesis Tracking (PMHT). An extension of PMHT for dealing
with classification measurements (PMHT-c) already exists. We show how PMHT-c can be applied to associate
chemical attributes to person tracks. This affords the localization of threads and a timely notification of the
security personnel.
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This paper presents procedures to calculate the probability that the measurement originating from an extraneous
target will be (mis)associated with a target of interest for the cases of Nearest Neighbor and Global association. It
is shown that these misassociation probabilities depend, under certain assumptions, on a particular - covariance
weighted - norm of the difference between the targets' predicted measurements. For the Nearest Neighbor
association, the exact solution, obtained for the case of equal innovation covariances, is based on a noncentral
chi-square distribution. An approximate solution is also presented for the case of unequal innovation covariances.
For the Global case an approximation is presented for the case of "similar" innovation covariances. In the general
case of unequal innovation covariances where this approximation fails, an exact method based on the inversion of
the characteristic function is presented. The theoretical results, confirmed by Monte Carlo simulations, quantify
the benefit of Global vs. Nearest Neighbor association. These results are applied to problems of single sensor as
well as centralized fusion architecture multiple sensor tracking.
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Small object detection with a low false alarm rate remains a challenge for automated hyperspectral detection algorithms
when the background environment is cluttered. In order to approach this problem we are developing a compact
hyperspectral sensor that can be fielded from a small unmanned airborne platform. This platform is capable of flying low
and slow, facilitating the collection of hyperspectral imagery that has a small ground-sample distance (GSD) and small
atmospheric distortion. Using high-resolution hyperspectral imagery we simulate various ranges between the sensor and
the objects of interest. This numerical study aids in analysis of the effects of stand-off distance on detection versus false
alarm rates when using standard hyperspectral detection algorithms. Preliminary experimental evidence supports our
simulation results.
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Sensor jitter introduces non-white noise fluctuations in imagery of cluttered scenes. These fluctuations are a major
source of interference in the detection of weak time-dependent signals, which may be associated with a subject's
appearance, motion, or brightness modulation. Due to the presence of sensor pattern noise and uncertainty in the
scene's subpixel spatial structure, standard frame-to-frame registration methods have limited ability to model and
remove these fluctuations. A simple temporal whitening approach, applicable to a wide variety of imaging systems, is
found to be highly effective for suppressing subpixel jitter effects, leading to dramatic (up to several orders of
magnitude) improvement in signal detection ability.
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In this paper a real-time cooperative path decision algorithm for UAV surveillance is proposed. The surveillance
mission includes multiple objectives: i) Navigate the UAVs safely in a hostile environment; ii) Search for new
targets in the surveillance region; iii) Classify the detected targets; iv) Maintain tracks on the detected targets.
To handle these competing objectives, a layered decision framework is proposed, in which different objectives are
relevant at different decision layers according to their priorities. Compared to previous work, in which multiple
objectives are integrated into a single global objective function, this layered decision framework allows detailed
specification of the desired performance for each objective and guarantees that an objective with high priority will
be first satisfied by eliminating possible compromises from other less important ones. In addition, path decision
strategies that are suited to individual objectives can be used at different decision layers. The layered decision
framework, along with a multi-step look-ahead path decision strategy based on a Roll-out policy is shown to be
able to guide the UAV group effectively for the multi-objective surveillance in a hostile environment.
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Passive coherent location (PCL), which uses the commercial signals as illuminators of opportunity, is an emerging
technology in air defense systems. The advantages of PCL are low cost, low vulnerability to electronic counter
measures, early detection of stealthy targets and low-altitude detection. However, limitations of PCL include lack
of control over illuminators, poor bearing accuracy, time-varying sensor parameters and limited observability.
In this paper, multiple target tracking using PCL with high bearing error is considered. In this case, the
challenge is to handle high nonlinearity due to high measurement error. In this paper, we implement the
converted measurement Kalman filter, unscented Kalman filter and particle filter based PHD filter for PCL
radar measurements and compare their performances.
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Bias estimation using objects with unknown data association requires concurrent estimation of both biases and optimal
data association. This report derives maximum a posteriori (MAP) data association likelihood ratios for concurrent bias
estimation and data association based on sensor-level track state estimates and their joint error covariance. Our approach
is unique for two reasons. First, we include a bias prior that allows estimation of absolute sensor biases, rather than just
relative biases. Second, we allow concurrent bias estimation and association for an arbitrary number of sensors. The
two-sensor likelihood ratio is derived as a special case of the general M-sensor result.
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In multi-sensor tracking systems, observations are often exchanged over a network for processing. Network delays
create situations in which measurements arrive out-of-sequence. The out-of-sequence measurement (OOSM)
update problem is of particular significance in networked multiple hypothesis tracking (MHT) algorithms. The
advantage of MHT is the ability to revoke past measurement assignment decisions as future information becomes
available. Accordingly, we not only have to deal with network delays for initial assignment, but must also address
delayed assignment revocations. We study the performance of extant algorithms and two algorithm modifications
for the purpose of OOSM filtering in MHT architectures.
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This paper presents a new small target detection method using scale invariant feature. Detecting small targets whose
sizes are varying is very important to automatic target detection in infrared search and track (IRST). The conventional
spatial filtering methods with fixed sized kernel show limited target detection performance for incoming targets. The
scale invariant target detection can be defined as searching for maxima in the 3D (x, y, and scale) representation of an
image with the Laplacian function. The scale invariant feature can detect different sizes of targets robustly. Experimental
results with real FLIR images show higher detection rate and lower false alarm rate than conventional methods.
Furthermore, the proposed method shows very low false alarms in scan-based IR images than conventional filters.
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