Michael J. Minardi

A challenge problem for 2D/3D imaging of targets from a volumetric data set in an urban environment

This paper describes a challenge problem whose scope is the 2D/3D imaging of stationary targets from a volumetric data set of X-band Synthetic Aperture Radar (SAR) data collected in an urban environment. The data for this problem was collected at a scene consisting of numerous civilian vehicles and calibration targets. The radar operated in circular SAR mode and completed 8 circular flight paths around the scene with varying altitudes. Data consists of phase history data, auxiliary data, processing algorithms, processed images, as well as ground truth data. Interest is focused on mitigating the large side lobes in the point spread function. Due to the sparse nature of the elevation aperture, traditional imaging techniques introduce excessive artifacts in the processed images. Further interests include the formation of highresolution 3D SAR images with single pass data and feature extraction for 3D SAR automatic target recognition applications. The purpose of releasing the Gotcha Volumetric SAR Data Set is to provide the community with X-band SAR data that supports the development of new algorithms for high-resolution 2D/3D imaging.

A challenge problem for SAR-based GMTI in urban environments

This document describes a challenge problem whose scope is the detection, geolocation, tracking and ID of moving vehicles from a set of X-band SAR data collected in an urban environment. The purpose of releasing this Gotcha GMTI Data Set is to provide the community with X-band SAR data that supports the development of new algorithms for SAR-based GMTI. To focus research onto specific areas of interest to AFRL, a number of challenge problems are defined. The data set provided is phase history from an AFRL airborne X-band SAR sensor. Some key features of this data set are two-pass, three phase center, one-foot range resolution, and one polarization (HH). In the scene observed, multiple vehicles are driving on roads near buildings. Ground truth is provided for one of the vehicles.

Ground moving target detection and tracking based on generalized SAR processing and change detection

A unified way of detecting and tracking moving targets with a SAR radar called SAR-MTI is presented. SAR-MTI differs from STAP or DPCA in that it is a generalization of SAR processing and can work with only a single phase center. SAR-MTI requires formation of a series of images assuming different sensor ground speeds, from vs-vtmax to vs+vtmax, where vs is the actual sensor ground speed and vtmax is the maximum target speed of interest. Each image will capture a different set of target velocities, and the complete set of images will focus all target speeds less than a desired maximum speed regardless of direction and target location. Thus the 2-dimensional SAR image is generalized to a 3-dimensional cube or stack of images. All linear moving targets less than the desired speed will be focused somewhere in the cube. The third dimension represents the along track velocity of the mover which is a piece of information not available to standard airborne MTI. A mover will remain focused at the same place within the cube as long as the motion of the mover and the sensor remain linear. Because stationary targets also focus within the detection cube, move-stop-move targets are handled smoothly and without changing waveforms or modes. Another result of this fact is that SAR-MTI has no minimum detectable velocity. SAR-MTI has an inherent ambiguity because the four-dimensions of target parameters (two dimensions in both velocity and position) are mapped into a three-dimensional detection space. This ambiguity is characterized and methods for resolving the ambiguity for geolocation are discussed. The point spread function in the detection cube is also described.

A Challenge Problem for SAR Change Detection and Data Compression.

This document describes a challenge problem whose scope is two-fold. The first aspect is to develop SAR CCD algorithms that are applicable for X-band SAR imagery collected in an urban environment. The second aspect relates to effective data compression of these complex SAR images, where quality SAR CCD is the metric of performance. A set of X-band SAR imagery is being provided to support this development. To focus research onto specific areas of interest to AFRL, a number of challenge problems are defined. The data provided is complex SAR imagery from an AFRL airborne X-band SAR sensor. Some key features of this data set are: 10 repeat passes, single phase center, and single polarization (HH). In the scene observed, there are multiple buildings, vehicles, and trees. Note that the imagery has been coherently aligned to a single reference.

Radar Signals Dismount Tracking for Urban Operations

It is critical in urban environments to not only track cars and tanks; but also individuals. Tracking dismounts, whereby an individual exits a car, can be done using conventional Electro-Optical (full color) or Infrared (thermal) cameras. However, EO/IR systems are subject to weather and line-of-sight conditions (i.e. person blocked by cloud) as well are degraded for long ranges. In this study, we pursue the use of radar images for dismount tracking. Radar dismount tracking will not entail the same robust features for person identification as EO systems; however, by being able to maintain track in all-weather conditions would afford friendly forces a location of all moving individuals. We show, using a feature-based tracker, that dismount detection, tracking, and potential intent, is possible. Radio Frequency (RF) tracking of dismounts is a relatively new concept because the data has not been available. By forming a data set based on the POSERTM program, and post-processing the data, we are interested in exploring the possibility of RF dismount tracking. In this paper, we (1) explore the generation of RF dismount data, (2) apply feature-based tracking algorithm to locate the moving target, and (3) assess the performance.

Spatially-varying calibration of along-track monopulse synthetic aperture radar imagery for ground moving target indication and tracking

In this research, we have developed an algorithm to reduce the residual artifacts of the background clutter (that is, stationary targets) that appear in the MTI imagery that are generated by Global Signal Subspace Difference (GSSD) of the monostatic and bistatic images of an along-track monopulse synthetic aperture radar (SAR) data. We have also established the theoretical foundation for estimating the motion track and parameters of the detected moving targets. We will show the results of these algorithms on measured SAR data.

SAR change detection MTI

This paper examines the theory, application, and results of using single-channel synthetic aperture radar (SAR) data with Moving Reference Processing (MRP) to focus and geolocate moving targets. Moving targets within a standard SAR imaging scene are defocused, displaced, or completely missing in the final image. Building on previous research at AFRL, the SAR-MRP method focuses and geolocates moving targets by reprocessing the SAR data to focus the movers rather than the stationary clutter. SAR change detection is used so that target detection and focusing is performed more robustly. In the cases where moving target returns possess the same range versus slow-time histories, a geolocation ambiguity results. This ambiguity can be resolved in a number of ways. This paper concludes by applying the SAR-MRP method to high-frequency radar measurements from persistent continuous-dwell SAR observations of a moving target.

Synthetic Aperture Radar moving target indication processing of along-track monopulse nonlinear gotcha data

This paper is concerned with imaging and moving target detection using a Synthetic Aperture Radar (SAR) platform that is known as Gotcha. The SAR platform can interrogate a scene using an imperfect circular trajectory; we refer to this as nonlinear SAR data collection. This collection can make monostatic and quasi-monostatic measurements in the along-track domain. We present subaperture-based wavefront reconstruction algorithms for motion compensation and imaging from this nonlinear SAR database. We also discuss adaptive filtering algorithms to construct MTI imagery from the two receiver channels of the system. Results will be provided.

Radar signals dismount data production

It has recently become apparent that dismount tracking from non-EO based sources will have a large positive impact on urban operations. EO / camera imaging is subject to line of site and weather conditions which makes it a non-robust source for dismount tracking. Other sensors exist (e.g. radar) to track dismount targets; however, little radar dismount data exists. This paper examines the capability to generate synthetic and measured dismount data sets for radar frequency (RF) processing. For synthetic data, we used the PoserTM program to generate 500 facet models of human dismount walking. Then we used these facet models with Xpatch to generate synthetic wideband radar data. For measured dismount data, we used a multimode (X-Band and Ku-Band) radar system to collect RF data of volunteer human (dismount) targets.

Detection and tracking of moving vehicles with Gotcha radar systems

The authors have defined an approach for detecting and tracking moving vehicles with Gotcha radar systems. Our approach exploits tracker feedback to address challenges for detection, false alarm mitigation, geolocation, and tracking in an urban surveillance environment. The authors presented a mathematical framework for processing multichannel SAR data in order to mitigate the combined effects of moving target defocus and strong clutter interference. The algorithm uses MRP adaptively in a STAP framework to focus up moving vehicles and enhance signal to clutter ratios. Using simulated data, we illustrated SAR defocus of an accelerating point scatterer and mitigation of the defocus using MRP. For this simulated example, we also presented the potential improvements to SINR loss using our adaptive processing compared to more standard STAP approaches.