Most designs for acoustic sensors perform some relatively simple level-based detection operation before applying more intensive resources to track the evolution of the target signature. This approach, followed in order to maintain false alarms at an acceptable level, results in the loss of information that could be derived while the signal is at levels below a set detection threshold and can result in missed or late opportunities for the all-important imaging sensors. The track-before-detect approach exploits the use of considering multiple data association alternatives forming potential tracks at low signal to noise ratios that get filtered on the basis of track dynamics to maintain an acceptable level of false alarms. This approach preserves the information derived in the early stages of track formation leading to a more complete exploitation of the available signal and result in earlier maturation of the track.
Border surveillance applications require low false alarm rates and long endurance. These requirements have not changed
since unattended ground sensors (UGS) were first used to monitor Viet Cong activity along the Ho Chi Minh Trail in the
1960's. However the targets are quite different today. Then the targets of interest were large military vehicles with strong
acoustic, seismic and magnetic signatures. Currently, the requirements imposed by new terrorist threats and illegal
border crossings have changed the emphasis to the monitoring of light vehicles and foot traffic. Unlike with military
driven requirements cost of ownership and ease of employment are at least as critical as sensor performance.
Unattended Ground Sensors (UGS) were first used to monitor Viet Cong activity along the
Ho Chi Minh Trail in the 1960's. In the 1980's, significant improvement in the capabilities of
UGS became possible with the development of digital signal processors; this led to their
use as fire control devices for smart munitions (for example: the Wide Area Mine) and later
to monitor the movements of mobile missile launchers. In these applications, the targets of
interest were large military vehicles with strong acoustic, seismic and magnetic signatures.
Currently, the requirements imposed by new terrorist threats and illegal border crossings
have changed the emphasis to the monitoring of light vehicles and foot traffic. These new
requirements have changed the way UGS are used. To improve performance against
targets with lower emissions, sensors are used in multi-modal arrangements. Non-imaging
sensors (acoustic, seismic, magnetic and passive infrared) are now being used principally
as activity sensors to cue imagers and remote cameras. The availability of better imaging
technology has made imagers the preferred source of "actionable intelligence". Infrared
cameras are now based on un-cooled detector-arrays that have made their application in
UGS possible in terms of their cost and power consumption. Visible light imagers are also
more sensitive extending their utility well beyond twilight. The imagers are equipped with
sophisticated image processing capabilities (image enhancement, moving target detection
and tracking, image compression). Various commercial satellite services now provide
relatively inexpensive long-range communications and the Internet provides fast worldwide
access to the data.
The promise of acoustic classification of vehicles is based on the expectations provided by the human ability to differentiate sounds from familiar vehicles. Some of these promises have not been fully achieved in practice, necessitating a "reality check" on the uses and limitations of the technology. Some of those limitations are: Operation in the real-world environment of outdoor propagation over rough terrain in the presence of natural and cultural background noise sources. A great number of the new applications are used against civilian-type vehicles that have emissions that are substantially lower than those of military vehicles. The success of speech recognition systems has also fueled some of these expectations. But before we take the analogy too far, we must note that there is a vast difference between the two tasks. Speech occupies a wider bandwidth than vehicle noise and is much more richly modulated than vehicle noises. Consequently there is much more information content to extract and more features to rely on than in the vehicle classification problem. Starting with a vehicle classification algorithm based on a neural network trained to recognize two different vehicles, we illustrate how by creating a continuous track on the target and integrating the output of the classifier over the life of the track we can improve the confidence of the classification results. Similarly fusing the results obtained by two or more sensors spread around the target can further improve the classification performance, cutting the rate of erroneous classifications by a third or more.
In this paper we examine the different functions performed by an acoustic-seismic unattended ground sensor (UGS) and how they contribute to the overall cost of the sensor and its implementation. A key point made is that the performance of the acoustic-seismic depends on target characteristics and environmental conditions and cannot easily be traded off against implementation costs. At the present time, sensor implementation costs are dominated by the radios. In general, it is best to use radios whose range is consistent with the detection capabilities of the sensors. Dense networks of sensors using short-range radios supporting distributed communication architecture will produce an unnecessary duplication of coverage and a much-increased cost. Different scenarios for the utilization of acoustic-seismic UGS are examined, and evaluated with respect to their implementation costs. The most cost-effective use of acoustic-seismic sensors is in those applications where they can use satellite communications or tap into an existing communications network.
Acoustic sensors have had a long history of use in military applications. Some of the factors favoring their use are: their ability to exploit loud and distinctive emissions of vehicles and weapons firings, their capability to detect and track targets in non line-of-sight conditions, and the ability to carry out their mission in a totally passive way (no emissions to give out their position). Acoustic-seismic sensors can also be implemented using low-power electronics. Acoustic-seismic sensors are now found in various surveillance sensors, generally known as Unattended Ground Sensors or UGS. The trend towards increasing computational capabilities, lower power consumption and better communications capacity have made these devices more useful and acceptable in a variety of military and peace-keeping operations. The promise of networked sensors has opened the possibility of large-scale sensor networks. However, we must be realistic about what can and cannot be achieved within the current technical horizon. The dream of “sensor dust”: miniature devices, built and deployed at minimum cost, transmitting volumes of data is at present, just that, a dream that has to be tempered with the realities imposed by physics.
changed substantially and that surveillance systems designed to operate against heavy armor following conventional tactics have to adapt to a “New Threat” environment. This observation is very pertinent to the use of acoustic and seismic sensors. These sensing modalities have been used with success against a threat consisting of armored vehicles powered by large Diesel engines. In the type of conflicts that we are likely to encounter in places like Afghanistan, Yemen and other SE Asian countries, US forces will be faced by a threat that can move about in converted civilian vehicles, Figure 1. Systems like the Wide Area Mine (a.k.a. Hornet) and the Brilliant Anti-Tank Munition (BAT) exploited the loud and distinctive signatures of combat vehicles to direct their fire control system. Other surveillance systems such as Steel Rattler / Steel Eagle and DARPA’s Micro-IUGS focused on “high value targets”, also military vehicles of considerable size. All of these target vehicles are powered by large, noisy diesel engines and have large tires or tracks that produce substantial seismic signatures.The object of this paper is to present an analysis of the signatures of civilian vehicles that may be adapted to military or terrorist activities. We will then look at the sensor configurations that are best adapted to detect and track these vehicles. Some examples of the type of surveillance data that we can obtain are also shown in the paper.
Technological advances in a number of fields have allowed SenTech to develop a highly capable Unattended Ground Sensor (UGS) able to perform a number of critical missions such as ground and air vehicle surveillance, personnel detection and tracking and sniper localization. These sensors have also been combined with electro-optic sensors to provide target images and improved tracking accuracy. Processing is done in a highly integrated processing module developed under DARPA's IUGS program. Acoustic sensors have been engineered to achieve a three-pound unit with a 15 day field life and long range VHF communications. These sensors will be delivered in early 2002 for testing during field exercises. Extensive testing of the algorithms and software has been conducted over the last few years at a variety of government-sponsored tests and demonstrations. A Gateway unit has been developed which can manage the operation of an eight-sensor field and perform two-dimensional sensor fusion.
Technological advances in a number of fields have allowed SenTech to develop a highly capable Unattended Ground Sensor (UGS) able to perform a number of critical missions such as ground and air vehicle surveillance, personnel detection and tracking and sniper localization. These sensors have also been combined with electro-optic sensors to provide target images and improved tracking accuracy. Processing is done in a highly integrated processing module developed under DARPA's IUGS program. Acoustic sensors have been engineered to achieve a three-pound unit with a 15 day field life and long range VHF communications. These sensors will be delivered in early 2002 for testing during field exercises. Extensive testing of the algorithms and software has been conducted over the last few years at a variety of government-sponsored tests and demonstrations. A Gateway unit has been developed which can manage the operation of an eight-sensor field and perform two-dimensional sensor fusion.
Persons or vehicles moving over ground generate a succession of impacts; these soil disturbances propagate away from the source as seismic waves. These seismic waves are especially useful in detecting footsteps which cannot be detected acoustically. Footstep signals can be distinguished from other seismic sources, such as vehicles or wind noise, by their impulsive nature. Even in noisy environments, statistical measures of the seismic amplitude distribution, such as kurtosis, can be used to identify a footstep. These detection methods can be used even with single component geophones. Moreover, the seismic signal is a vector wave that can be used to track the source bearing. To do such tracking a three-component measurement is needed. If multiple sources are separated in angle, we can use this bearing information to estimate the number of walkers.
This paper describes the design of a small sensor that can detect and track different targets, namely vehicles, personnel and sniper fire. Building on previous work with portable sensors using both seismic and acoustic transducers, the goal was to design a sensor with similar functions that fits in a small projectile deployed from a standard M203 grenade launcher. We discuss methods to reduce weight, size, and power consumption. We use a shell-within- shell design in which the instrument separates from the outer body at the apex of its flight. After the separation, spring loaded arms unfold from the inner body. The unfolding arms serve multiple purposes: to hold the acoustic transducers on the periphery of a small disk with a measurement aperture larger than the shell (about 5 times the shell diameter), to stabilize the sensor in flight, and to act as a ground plane for radio transmission. An example of a hand-emplaced version using the same processor is also discussed.
One can detect and track vehicles and personnel using a three-component seismic velocity transducer. Persons or vehicles moving over ground generate a succession of impacts; these soil disturbances propagate away from the source as seismic waves. Because the soil is an elastic medium both vertical and longitudinal waves propagate, diminishing in intensity as R-2. Furthermore because the surface of the soil is the boundary of an elastic space, a Rayleigh surface wave is also generated, diminishing in intensity as R-1. This surface wave is a vector wave that can be used to track the source. In addition to the classic model of surface waves on an elastic half space we discuss special features of seismic measurements. Among these are: contamination of the seismic signal by local acoustic waves, the excess non-geometric attenuation of the seismic signal, the influence of reflections from layered soil in tracking personnel, and finally a method of ranging using the periodic impact signature of vehicles.
Ground and air vehicles have distinctive acoustic signatures produced by their engines and/or propulsion mechanism. The structure of these signatures makes them amenable to classification by pattern recognition algorithms. There are substantial challenges in this process. Vehicle signatures are non-stationary by virtue of variations in engine RPM and maneuvers. Field sensors are also exposed to substantial amounts of noise and interference. We discuss the use of neural network techniques coupled with spatial tracking of the targets to carry out the target identification process with a high degree of accuracy. Generic classification is done with respect to the type of engine (number of cylinders) and specific classification is done for certain types of vehicles. This paper will discuss issues of neural network structure and training and ways to improve the reliability of the estimate through the integration of target tracking and classification algorithms.
Technologies for sniper localization have received increased attention in recent months as American forces have been deployed to various trouble spots around the world. Among the technologies considered for this task acoustics is a natural choice for various reasons. The acoustic signatures of gunshots are loud and distinctive, making them easy to detect even in high noise background environments. Acoustics provides a passive sensing technology with excellent range and non line of sight capabilities. Last but not least, an acoustic sniper location system can be built at a low cost with off the shelf components. Despite its many advantages, the performance of acoustic sensors can degrade under adverse propagation conditions. Localization accuracy, although good, is usually not accurate enough to pinpoint a sniper's location in some scenarios (for example which widow in a building or behind which tree in a grove). For these more demanding missions, the acoustic sensor can be used in conjunction with an infra red imaging system that detects the muzzle blast of the gun. The acoustic system can be used to cue the pointing system of the IR camera in the direction of the shot's source.
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