Recent technology developments in digital radio, low-cost inertial navigation systems and unmanned air vehicle design are converging to enable and make practical several new radar sensing modes such as simultaneous SAR/GMTI from persistent staring-mode radar, 3D SAR from a single-pass, single phase center radar and wide-angle radar tracking of dismounts. One of the challenges for algorithm developers is a lack of high-quality target and clutter signature data from the new radar modes. AFRL's Sensor Directorate and SET Corporation are developing a compact, low-cost wide-angle radar test bed capable of simulating a variety of radar modes, including 3D SAR, SAR/GMTI from staring-mode radar and ultra-fine resolution range-Doppler. We provide an overview of the wide-angle radar test bed architecture, its modular design and our implementation approach. We then describe several non-conventional wide-angle radar sensor modes and outline a corresponding series of test bed data collection experiments that could be used to support the development of new tracking and recognition algorithms.
The Center for Advanced Communications (CAC) at Villanova University has conducted several preliminary through-the-wall imaging experiments and collected real data on different settings behind the wall using a newly-integrated RF instrumentation suite. The full-polarization, 2D aperture data measurements are taken using an Agilent network analyzer, Model ENA 5071B, implementing a step frequency waveform over a 2-3 GHz frequency range. The room imaged is a typical computer lab that has been lined with radar absorbing material. Three different arrangements of the room's contents are considered: empty scene, calibration scene, and populated scene. The empty scene allows measurement of the noise/clutter background and supports coherent subtraction with the other two scenes. The calibration scene contains isolated reflectors that may be used to determine a fully-polarimetric radiometric calibration solution for the experimental system. The populated scene contains a number of common objects such as a phone, computer, tables, chair and filing cabinet. In addition, a jug of saline solution has been added to crudely approximate a human. Each scene is imaged with and without a wall. The wall is composed of plywood and gypsum board on a wood frame. The antennas are mounted on a 2D scanner that moves the antennas along and adjacent to the wall and is controlled by the network analyzer. Two additional antennas are fixed to the scanner frame and act as bistatic receivers. The paper provides a detailed description of the RF system and experimental conditions and provides a quick look at collected data products. The data measurements, technical details on collection instrumentation, auxiliary measurements, and scene truth data will be made available starting in April 05 to download from the Villanova CAC website at http://www.engineering.villanova.edu/cac/TWRI-experiments.
Targets may be more likely than non-targets to occur in groups. "Group detection" algorithms exploit this property of target behavior to improve the performance of a detection system. This paper develops some of the issues to be addressed when assessing the performance of a group detection algorithm. Two basic cases are considered, one where object detection is the goal (with group detection as an intermediate tool) and one where group detection is directly the goal. To understand the benefits of group detection algorithms in object detection, we propose considering pre-group to post-group object-level false alarm rate at a fixed detection probability. To understand the relative ease of group detection as an end in itself versus object detection, object-level Receiver Operating Characteristic (ROC) curves may be compared to group-level ROCs. The significance of the assessment approach is demonstrated, where different assessment approaches can produce apparent benefits that differ by several orders-of-magnitude. In addition to the methodology dependence, performance has the usual dependence on operating conditions (OCs), including the target grouping behavior (frequency of group sizes, spatial separation, and mismatch between model and reality), spatial dependence in clutter objects, and the pre-group object-level ROC (which in-turn depends on classical OCs).
The AFRL COMPASE Center has developed and applied a disciplined methodology for the evaluation of recognition systems. This paper explores an element of that methodology related to the confusion matrix as a tabulation of experiment outcomes and its corresponding summary performance measures. To this end, the paper introduces terminology and the confusion matrix structure for experiment results. It provides several examples - from current Air Force programs - of summary performance measures and their relationship to the confusion matrix. Finally it considers the advantages and disadvantages of these summary performance measures and points to effective strategies for selecting such measures.
Early in almost every engineering project, a decision must be made about tools; should I buy off-the-shelf tools or should I develop my own. Either choice can involve significant cost and risk. Off-the-shelf tools may be readily available, but they can be expensive to purchase and to maintain licenses, and may not be flexible enough to satisfy all project requirements. On the other hand, developing new tools permits great flexibility, but it can be time- (and budget-) consuming, and the end product still may not work as intended. Open source software has the advantages of both approaches without many of the pitfalls. This paper examines the concept of open source software, including its history, unique culture, and informal yet closely followed conventions. These characteristics influence the quality and quantity of software available, and ultimately its suitability for serious ATR development work. We give an example where Python, an open source scripting language, and OpenEV, a viewing and analysis tool for geospatial data, have been incorporated into ATR performance evaluation projects. While this case highlights the successful use of open source tools, we also offer important insight into risks associated with this approach.
In this paper we describe the results of our investigation into the intra-class variability of a vehicle class (T-72 Tanks) from the perspective of an Automatic Target Recognition system. We examine the performance of synthesized vehicle models for ATR systems and demonstrate that these models fall within the bounds of the vehicle class set by the intra-class variability of the vehicle. We then demonstrate the relevance of the mean-square-error between an image chip and a template as a useful measure of distance between the two vehicles. We also show that it is possible to constitute a superior class representative and classifier by combining chips from two different vehicles while constructing the templates.
We present a family of polarimetric generalized likelihood ratio tests (PGLRTs) which exploit fully polarimetric information in a high resolution application to detect scattering centers in terrain clutter. The detectors are based on a deterministic target model derived from the Huynen parameterization of a scattering matrix. The model is parameterized by target amplitude, absolute phase, and target orientation angle. These parameters, which are unknown in many practical applications, are estimated by the detectors. the PGLRTs may be used to enhance the responses of certain scattering center types relative to others in a given region of interest. Once a scattering center is detected, the ML estimates formed by a PGLRT may be used to further describe the detected target. We implement and analyze the performance of the PGLRTs designed for Gaussian and K-distributed clutter with known covariance. The PGLRT that assumes all three model parameters are unknown is a detector whose performance we show to lie between that of the optimal polarimetric detector and the polarization whitening filter.