Recent work has revealed that an adaptation of phase gradient autofocus (PGA) techniques can be used to refocus the signature smears induced by moving targets in synthetic aperture radar (SAR) images. In this approach, the residual range migration errors induced by target motion are estimated and corrected within the phase history data (PHD) domain. The resulting quality of the target images generated using this PHD-based autofocus methodology varies according to the selected region-of-interest (ROI) as input into the processing. The present analysis investigates the variation in the quality of the refocused target images with regards to ROI selection.
This investigation considers the shapes of synthetic aperture radar (SAR) imagery signature smears that are caused by surface targets that perform braking maneuvers during the SAR collection time. It is known that such maneuvering target signatures can have a wide variety of two-dimensional (2D) shapes, as opposed to the simpler parabolic signatures that are induced by constant velocity targets. The current paper examines the theoretical properties of these 2D signature shapes for cases in which the specific parameters of the target braking maneuver temporal profile are varied, including the rate at which the target decreases speed, the total amount of speed change, and the speed transition time within the SAR collection interval. Furthermore, the current investigation yields new insights regarding the complicated SAR signature shapes that are indicative of targets undergoing such braking maneuvers. This analysis reveals that the SAR signature for a given braking target is effectively a composite of three curved smear portions. One portion is a part of a parabola that is obtained from the constant-velocity target motion at the initial SAR collection time. Next, the second portion is that of a part of a different parabola that is generated from the final constant target velocity segment during the SAR collection interval. The third curved portion of the full moving target signature forms a connection between the parts of the two parabolas that are due to the initial and final constant velocity segments of the full target motion during the SAR measurement interval.
This paper examines the implications pertaining to the problem of attempting to invert synthetic aperture radar (SAR) measurement data to yield unique estimates of the underlying motion of slow targets in the imaged scene. A recent analysis has demonstrated that ambiguities exist in estimating the kinematics parameters of surface targets for general bistatic SAR collection data. In particular, a procedure has been developed which generates alternate target trajectories which give the same SAR measurements as that of the true target motion. The current paper extends the earlier analysis by generating specific numeric examples of alternate target trajectories corresponding to the motion of a given slowly moving target. This slow-target case reveals the counter-intuitive result that a single SAR collection data set can be generated by target trajectories with significantly different, and possibly opposing, heading directions. For example, the true motion of a given target can be moving towards the mean radar position during the SAR collection interval, whereas a valid alternate trajectory can correspond to a target that is moving away from the radar. The present analysis demonstrates the extent of the challenges associated with attempting to estimate of the underlying motion of targets using SAR measurement data.
This paper investigates methodologies for predicting the smear signatures in squinted spotlight synthetic aperture radar (SAR) imagery collections due to surface targets that are undergoing braking maneuvers. Previous analysis considered the case of broadside collection geometries. Analytic computation of a power series expansion is used to compute a generic expression for the down-range and cross-range components of the predicted mover signature. In addition, recent analysis presents capabilities for predicting the full signature shape, including the smear width and interference effects. The current investigations focuses on the effects of squinted collection geometries upon braking targets signatures.
This paper investigates methodologies for predicting the smear signatures in broadside spotlight synthetic aperture radar imagery collections due to surface targets that are undergoing turning maneuvers. This analysis examines the case of broadside geometry wherein the radar moves with constant speed and heading on a level flight path. This investigation concentrates moving target smear issues that yield some defocus in the range direction, although much smaller in magnitude than the motion induced smearing in the radar cross-range direction. This paper focuses on the case of a target that executes a turning maneuver during the SAR collection interval. The SAR simulations are shown to give excellent agreement between the moving target signatures and the predicted shapes of the central contours.
This paper investigates techniques for using low probability of intercept (LPI) modulation techniques for forming synthetic aperture radar (SAR) imagery. This analysis considers a specific waveform type based upon Frank codes in providing for the LPI capability via phase shift keying (PSK) modulation. A correlation receiver that is matched to the transmitted waveform is utilized to generate a set of SAR data. This analysis demonstrates the ability to form SAR images based upon simulated radar measurements collected by a notional radar sensor that has ability to transmit and receive Frank-coded waveforms and to form SAR images based upon the results of a correlation receiver. Spotlight-mode SAR images are generated using the Frank-coded waveforms and their properties are analyzed and discussed.
This paper examines the signature characteristics of moving targets in spotlight synthetic aperture radar (SAR) image data. This analysis considers the special case in which the radar sensor is assumed to move with constant speed and heading on a level flight path with broadside imaging geometry. It is shown that the resulting defocused smear signature in the spotlight SAR image exhibits range migration effects, as has been shown previously for strip map SAR analysis. In particular, cases of uniform target motion exhibit simply curved range migration paths, whereas non-uniform target motion can cause complicated smear shapes.
Today's 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.
Video image exploitation is an increasingly crucial component of battlefield surveillance systems. In order to address the present difficulties pertaining to video exploitation of tactical sensors, DARPA has developed the Airborne Video Surveillance (AVS) program. AVS will utilize Electro-Optical (EO) and Infrared (IR) video imagery similar to that available from current and future Unmanned Aerial Vehicle (UAV) systems. The AVS program will include the development, integration, and evaluation of technologies pertaining to precision video registration, multiple target surveillance, and automated activity monitoring into a system capable of real-time UAV video exploitation. When combined with existing EO and IR target recognition algorithms, AVS will provide the Warfighter with a comprehensive video battlespace awareness capability.
In today's 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.