D.A. Garren

Full-polarization matched-illumination for target detection and identification

This paper investigates the optimization of the full-polarization radar transmission waveform and the receiver response to maximize either target detection or identification performance. Application of such full-polarization matched-illumination techniques to simulated VHF-band frequency response data of mobile surface targets yields a significant performance improvement over that corresponding to chirped full-polarization transmission waveforms.

Ground target tracking-a historical perspective

Airborne sensor platforms such as Joint STARS provide a capability for ground surveillance and monitoring target movements. Because of the high target density and maneuverability, high clutter, low visibility due to terrain masking, etc., ground target tracking presents unique challenges not present in tracking other types of targets. This paper reviews major developments in multi-target tracking over the past four decades and discusses how algorithms developed primarily for tracking air targets can be used for tracking ground targets. The similarities and differences between found and air target tracking are first compared. We then discuss how simple target state estimation algorithms such as Kalman filtering have evolved into more complicated algorithms for tracking maneuvering targets. Similarly, we discuss how association algorithms have progressed from nearest neighbor to joint probabilistic data association (JPDA), multiple hypothesis tracking (MHT), and multi-dimensional assignment. The adequacy of these techniques for tracking ground targets is discussed.

Optimal transmission pulse shape for detection and identification with uncertain target aspect

This paper investigates the optimization of the transmission radar pulse shape to maximize either target detection or identity discrimination between the T-72 and M1 tanks under conditions of aspect uncertainty. Significant performance improvements in detection and identification are obtained using optimized transmission waveforms over that of standard chirped pulses.

Image preconditioning for a SAR image reconstruction algorithm for multipath scattering

Recent analysis has resulted in an innovative technique for forming synthetic aperture radar (SAR) images without the multipath ghost artifacts that arise in traditional methods. This technique separates direct-scatter echoes in an image from echoes that are the result of multipath, and then maps each set of reflections to a metrically correct image space. Current processing schemes place the multipath echoes at incorrect (i.e., ghost) locations due to fundamental assumptions implicit in conventional array processing. Two desired results are achieved by use of this image reconstruction algorithm for multipath scattering (IRAMS). First, the intensities of the ghost returns are reduced in the primary image space, thereby improving the relationship between the image pattern and the physical distribution of the scatterers. Second, a higher dimensional image space that enhances the intensities of the multipath echoes is created which offers the potential of dramatically improving target detection and identification capabilities. This paper develops techniques in order to precondition the input images at each level and each offset in the IRAMS architecture in order to reduce multipath false alarms.

AMSTE precision fire control tracking overview

The DARPA Affordable Moving Surface Target Engagement (AMSTE) program is a research effort to develop a system concept to track and engage a moving surface target with a single unitary warhead. Engaging a moving surface target requires precision tracking to a degree that has not been demonstrated in previous work. Tracking moving surface targets is especially difficult because of the targets ability to maneuver in unpredictable ways, such as coming to a stop, and the potential for a large number of nearby confusing targets. Because of the importance of tracking to the overall AMSTE system, a significant portion of the program is dedicated to developing advanced tracking algorithms. This paper will briefly describe the current state of the art of surface target trackers developed under the DARPA Moving Target Exploitation (MTE) program. It then identifies the inadequacies in association and bias removal that must be overcome to obtain the high fidelity tracks required for weapon targeting. The improved tracking techniques discussed include the use of data from multiple Ground Moving Target Indicator (GMTI) sensors to improve location accuracy, bias removal techniques, advanced multiple hypothesis trackers, variable state interacting multiple models using higher order motion models, feature aided tracking and terrain analysis.

Theoretical analysis of a continuous tracking system

Todays radar exploitation system utilize information from both Ground Moving Target Indication (GMTI) and Synthetic Aperture Radar (SAR) obtained from various airborne platforms. GMTI detects and supports the classification of moving targets, whereas SAR detects and supports the classification of stationary targets. However, there is currently no ability to integrate the information from these two classes of radars in tracking targets that execute sequences of move-stop-move maneuvers. The solutions of this dilemma is the development of a Continuous Tracking (CT) architecture that uses distinctive GMTI and SAR features to associate stationary and moving target detections through move-stop-move maneuvers. This paper develops a theoretical model and present corresponding numeric computations of the performance of the CT syste. This theory utilizes a two- state Markov process to model the successive SAR and MTI detections are derived from typical traffic and sensor behaviors. This analysis of the sensor characteristics and the underlying traffic model provides a foundation in designing a CT systems with the maximum possible performance.

Development of novel transmit waveforms for SAR-based target detection

This paper demonstrates methods of optimizing a sequence of radar transmit waveforms for a synthetic aperture radar (SAR) based upon specific knowledge of a targets radar cross section (RCS). The intent is to maximize target detection performance. It is required to have a priori knowledge of targets radar cross section (RCS). Two approaches are evaluated, the matched waveform and the variable chirp waveform. Examples are shown for each approach for a synthetically generated target data set.

Battlespace exploitation visualization

In todays imaging paradigm, each platform feds a single exploitation feeds a single exploitation systems a single sensor data stream. Currently, there is no ability to integrate the many exploitation capabilities arising from the ever-increasing number of imaging platforms. The solution to this dilemma is the development of a battlespace exploitation visualization environment (BEVE) capable of providing real-time visualization of multi-sensor data streams to image analysts (IAs). The vision of BEVE is a system receiving a variety of imaging data types, integrating the results of a data fusion analysis, and visually fusing this data into a variety of exploitable visualizations. This paper discuses three primary technologies related to BEVE: the processing of the input sensor data, the visualization technologies, and the interpretation and interaction with the IA.