Detecting and tracking a moving ground target in radar imagery is a challenge intensified by clutter, sensor anomalies, and the substantial signature variations that occur when a target's aspect angle changes rapidly. In its GMTI mode, a radar produces range-Doppler images that contain both kinematic reports and shape features. An HRR signature, when formed as the Fourier transform of the range-Doppler image across its Doppler dimension, becomes a derived measurement and an alternative source of identity information. Although HRR signatures can vary enormously with even small changes in target aspect, such signatures were vital for associating kinematic reports to tracks in this work. This development started with video phase history (VPH) data recorded from a live experiment involving a GMTI radar viewing a single moving target. Since the target could appear anywhere in the range-Doppler image derived from the VPH data, the goal was to localize it in a small range-Doppler "chip" that could be extracted and used in subsequent research. Although the clutter in any given VPH frame generally caused false chips to be formed in the full range-Doppler image, at most one chip contained the target. The most effective approach for creating any chip is to ensure that the object is present in the return from each pulse that contributes to that chip, and to correct any phase distortions arising from range gate changes. Processing constraints dictated that the algorithm for target chip extraction be coded in MATLAB with a time budget of a few seconds per frame. Furthermore, templates and shape models to describe the target were prohibited. This paper describes the nonlinear filtering approach used to reason over multiple frames of VPH data. This nonlinear approach automatically detects and segments potential targets in the range-Doppler imagery, and then extracts kinematic and shape features that are tracked over multiple data frames to ensure that the real target is in the declared chip. The algorithm described was used successfully to process over 84,000 frames of real data without human assistance.
Accurately associating sensor kinematic reports to known tracks, new tracks, or clutter is one of the greatest obstacles to effective track estimation. Feature-aiding is one technology that is emerging to address this problem, and it is expected that adding target features will aid report association by enhancing track accuracy and lengthening track life. The Sensor's Directorate of the Air Force Research Laboratory is sponsoring a challenge problem called Feature-Aided Tracking of Stop-move Objects (FATSO). The long-range goal of this research is to provide a full suite of public data and software to encourage researchers from government, industry, and academia to participate in radar-based feature-aided tracking research. The FATSO program is currently releasing a vehicle database coupled to a radar signature generator. The completed FATSO system will incorporate this database/generator into a Monte Carlo simulation environment for evaluating multiplatform/multitarget tracking scenarios. The currently released data and software contains the following: eight target models, including a tank, ammo hauler, and self-propelled artillery vehicles; and a radar signature generator capable of producing SAR and HRR signatures of all eight modeled targets in almost any configuration or articulation. In addition, the signature generator creates Z-buffer data, label map data, and radar cross-section prediction and allows the user to add noise to an image while varying sensor-target geometry (roll, pitch, yaw, squint). Future capabilities of this signature generator, such as scene models and EO signatures as well as details of the complete FATSO testbed, are outlined.
Many years of tracking research have shown that the greatest obstacle to effective track estimation is accurately associating sensor kinematic reports to known tracks, new tracks, or clutter. Errors in report association occur more frequently under increasingly stressful conditions, like closely-spaced targets and low measurement rates, which can lead to unstable and even divergent tracking performance. It is widely expected that adding target features will aid report association and result in enhanced track accuracy and lengthened track life. Although sensors can provide features to enhance association, progress in implementing feature aiding has been slowed by the lack of data and tools that could assist exploration and algorithm development. To encourage research in this important discipline, the Sensors Directorate of the Air Force Research Laboratory (AFRL/SN) is sponsoring a challenge problem called Feature-Aided Tracking of Stop-move Objects (FATSO). FATSO's long-range goal is to provide a full suite of public data and software to promote explorations into viable methods of feature aiding. This paper introduces the FATSO project, focusing on an upcoming release that will contain data from a diverse target set and predictor software for generating radar signatures.
“Robust identification” in SAR ATR refers to the problem of determining target identity despite the confounding effects of “extended operating conditions” (EOCs). EOC’s are statistically uncharacterizable SAR intensity-signature variations caused by mud, dents, turret articulations, etc. This paper describes a robust ATR approach based on the idea of (1) hedging against EOCs by attaching “random error bars” (random intervals) to each value of the image likelihood function; (2) constructing a “generalized likelihood function” from them; and (3) using a set-valued, MLE-like approach to robustly estimate target type. We compare three such classifiers, showing that they outperform conventional approaches under EOC conditions.
For the past two years in this conference, we have described techniques for robust identification of motionless ground targets using single-frame Synthetic Aperture Radar (SAR) data. By robust identification, we mean the problem of determining target ID despite the existence of confounding statistically uncharacterizable signature variations. Such variations can be caused by effects such as mud, dents, attachment of nonstandard equipment, nonstandard attachment of standard equipment, turret articulations, etc. When faced with such variations, optimal approaches can often behave badly-e.g., by mis-identifying a target type with high confidence. A basic element of our approach has been to hedge against unknowable uncertainties in the sensor likelihood function by specifying a random error bar (random interval) for each value of the likelihood function corresponding to any given value of the input data. Int his paper, we will summarize our recent results. This will include a description of the fuzzy maximum a posteriori (MAP) estimator. The fuzzy MAP estiamte is essentially the set of conventional MAP estimates that are plausible, given the assumed uncertainty in the problem. Despite its name, the fuzzy MAP is derived rigorously from first probabilistic principles based on random interval theory.
This paper develops a multiple-frame multiple-hypothesis tracking (MF-MHT) method and applies it to the problem of maintaining track on a single moving target from dim images of the target scene. From measurements collected over several frames, the MF-MHT method generates multiple hypotheses concerning the trajectory of the target. Taken together, these hypotheses provide a smoothed and reliable estimate of the target state. This work supports TENET, an Air Force Research Lab. Project that is developing nonlinear estimation techniques for tracing. TENET software was used to simulate both target dynamics and sensor measurements over a series of Monte Carlo experiments conducted at various signal-to-noise ratios (SNRs). Results are presented that compare computational complexity and accuracy of MF-MHT to two previously-documented nonlinear approaches to predetection tracking, a finite difference scheme and a particle filter method. Results show that MF-MHT requires about 2-3 dB more SNR to compete with the nonlinear methods on an equal footing.
We describe ongoing work in applying Finite Set Statistics (FISST) techniques to a Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) problem. It summarizes recent results in an ongoing project in which we are applying FISST filtering approaches to the problem of identifying ground targets from Synthetic Aperture Radar. The signatures for these targets are ambiguous because of extended operating conditions, that is the images have uncharacterizeable noise introduced in the form of mud, dents, etc. We propose a number of mechanisms for compensating for this noise.
During the last two decades IR Goodman, HT Nguyen and others have shown that several basic aspects of expert-systems theory-fuzzy logic, Dempster-Shafer evidence theory, and rule-based inference-can be subsumed within a completely probabilistic framework based on random set theory. In addition, it has been shown that this body of research can be rigorously integrated with multisensor, multitarget filtering and estimation using a special case of random set theory called 'finite-set statistics' (FISST). In particular, FISST allows the basis for standard tracking and ID algorithms-nonlinear filtering theory and estimation theory; to be extended to the case when evidence can be highly 'ambiguous' because of extended operating conditions, e.g. when images are corrupted by effects such as dents, mud etc. This paper extends those results by showing that the technique is relatively insensitive to the uncertainty model used to construct the ambiguous likelihood function.
Previous nonlinear filtering research has shown that by directly estimating the probability density of the target state, weak and closely spaced targets can be tracked without performing data association. Data association imposes a heavy burden, both in its design where complex data management structures are required and in its execution which often requires many computer cycles. Therefore, avoiding data association can have advantages. However, some have suggested that data association is required to estimate and correct sensor biases that are nearly always present so avoiding it is not a practical option. This paper demonstrates that target numbers, target tracks, and sensor biases can all be estimated simultaneously using association-free nonlinear methods, thereby extending the useful range of these methods while preserving their inherent advantages.
The work presented here is pat of a generalization of Bayesian filtering and estimation theory to the problem of multisource, multitarget, multi-evidence unified joint detection, tracking, and target ID developed by Lockheed Martin Tactical Defense Systems and Scientific Systems Co., Inc. Our approach to robust joint target identification and tracking was to take the StaF algorithm and integrate it with a Bayesian nonlinear filter, where target position, velocity, pose, and type could then be determined simultaneously via maximum a posteriori estimation. The basis for the integration between the tracker and classifier is base don 'finite-set statistics' (FISST). The theoretical development of FISST is a Lockheed Martin ongoing project since 1994. The specific problem addressed in this paper is that of robust joint target identification and tracking via fusion of high range resolution radar (HRRR) - from the automatic radar target identification (ARTI) data base - signatures and radar track data. A major problem in HRRR ATR is the computational load created by having to match observations against target models for every feasible pose. If pose could be estimated efficiently by a filtering algorithm from track data, the ATR search space would be greatly reduced. On the other hand, HRRR ATR algorithms produce useful information about pose which could potentially aid the track-filtering process as well. We have successfully demonstrated the former concept of 'loose integration' integrating the tracker and classifier for three different type of targets moving on 2D tracks.
During the last two decades I.R. Goodman, H.T. Nguyen and others have shown that several basic aspects of expert- systems theory-fuzzy logic, Dempster-Shafer evidence theory, and rule-based inference-can be subsumed within a completely probabilistic framework based on random set theory. In addition, it has been shown that this body of research can be rigorously integrated with multisensor, multitarget filtering and estimation using a special case of random set theory called `Finite-Set Statistics' (FISST). In particular, FISST allows the basis for standard tracking and I.D. algorithms--nonlinear filtering theory and estimation theory--to be extended to the case when evidence can be highly `ambiguous' (imprecise, vague, contingent, etc.). This paper summarizes preliminary results in applying the FISST filtering approach to the problem of identifying ground targets from Synthetic Aperture Radar data that is `ambiguous' because of Extended Operating Conditions, e.g. when images are corrupted by effects such as dents, mud, etc.
A Joint Multitarget Probability (JMP) is a posterior probability density p<SUB>T</SUB>(x<SUB>1</SUB>,...,x<SUB>T</SUB>Z) that there are T targets (T an unknown number) with unknown locations specified by the multitarget state X equals (x<SUB>1</SUB>,...,x<SUB>T</SUB>)<SUP>T</SUP> conditioned on a set of observations Z. This paper presents a numerical approximation for implementing JMP in detection, tracking and sensor management applications. A problem with direct implementation of JMP is that, if each x<SUB>t</SUB>, t equals 1,...,T, is discretized on a grid of N elements, N<SUP>T</SUP> variables are required to represent JMP on the T-target sector. This produces a large computational requirement even for small values of N and T. However, when the sensor easily separates targets, the resulting JMP factorizes and can be approximated by a product representation requiring only O(T<SUP>2</SUP>N) variables. Implementation of JMP for multitarget tracking requires a Bayes' rule step for measurement update and a Markov transition step for time update. If the measuring sensor is only influenced by the cell it observes, the JMP product representation is preserved under measurement update. However, the product form is not quite preserved by the Markov time update, but can be restored using a minimum discrimination approach. All steps for the approximation can be performed with O(N) effort. This notion is developed and demonstrated in numerical examples with at most two targets in a 1-dimensional surveillance region. In this case, numerical results for detection and tracking for the product approximation and the full JMP are very similar.
Several sensor management schemes based on information theoretic metrics such as discrimination gain have been proposed, motivated by the generality of such schemes and their ability to accommodate mixed types of information such as kinematic and classification data. On the other hand, there are many methods for managing a single sensor to optimize detection. This paper compares the performance against low signal-noise ratio targets of a discrimination gain scheme with three such single sensor detection schemes: the Wald test, an index policy that is optimal under certain circumstances and an 'alert-confirm' scheme modeled on methods used in some existing radars. For the situation where the index policy is optimal, it outperforms discrimination gain by a slight margin. However, the index policy assumes that there is only one target present. It performs poorly when there are multiple targets while discrimination gain and the Wald test continue to perform well. In addition, we show how discrimination gain can be extended to multisensor/multitarget detection and classification problems that are difficult for these other methods. One issue that arises with the use of discrimination gain as a metric is that it depends on both the current density and an a priori distribution. We examine the dependence of discrimination gain on this prior and find that while the discrimination depends on the prior, the gain is prior-independent.