KEYWORDS: Data fusion, Bayesian inference, Kinematics, Data modeling, Signal processing, Data processing, Radar, Detection and tracking algorithms, Sensors, Americium
Data fusion concepts have been applied in many disciplines, but a general systematic formulation has not been well developed. This paper is intended to provide a guideline in applying data fusion techniques to a practical problem, the fusion of target identification (ID) attributes measurements. Formation of a consensus function is first presented, and is followed by construction of a hierarchical probabilistic network for computing a joint probability density. An ID fusion processing approach is described and integrated into a generalized track/data association algorithm.
KEYWORDS: Data processing, Signal processing, Radar, Personal digital assistants, Target detection, Sensors, Detection and tracking algorithms, Motion models, Neural networks, Surveillance
A neural data association process during the motion perception which may occur at a low level in the human visual system is described. Principles of the neural process are then applied to the data association (DA) problem arising in radar target tracking. After reviewing radar tracking operations and problems with current DA algorithms, a new biological model based approach is presented.
An attempt is made to combine a deterministic digital filter and a stochastic
filter. The state equation is the standard form used for a Kalman filter deriva—
tion and the output equation is of the finite impulse response filter. An optimal
estimator is derived for this combined structure, called a finite impulse response
estimator (FIRE) which permits processing of a signal contaminated by deterministic
and random noises. Derivation of the FIRE utilizes the state augmentation technique
and the innovation technique. The proposed method is straight forward and
easy to implement and it can be applied to areas such as time varying signal processing
or target tracking where radar 'returns are contaminated by low frequency
noises. Full derivation and a tracking application are presented.
In target environments that include Electronic Countermeasures (ECM), vhere
the availability of radar range measurements is severely reduced, an integration
of ESM into a multi-sensor tracker provides an inexpensive but effective approach
to augmenting radar tracking systems. Tracking filter equations are presented for a
multi heterogeneous-sensor tracking system consisting of radars and ESM sensors.
Measurement time-based track updating is performed, due to non—periodicity of ESM
measurement acquisition times, instead of usual track—based updates. Track
updating using ESM measurements is accomplished via an EKF-based algorithm and
radar measurements via an tracking algorithm. The integration of data from
different sensor types is a logical response to increasingly hostile airborne
threats. The technique described in this paper is a straight forward and costeffective
approach for accomplishing that integration.
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