Stanton Musick

A simulation system for feature-aided tracking research

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). FATSOs 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 SAR ATR via set-valued classifiers: new results

“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.

Bias Estimation in an Association-Free Nonlinear Filter☆

Abstract Musick, S., and Kastella, K., Bias Estimation in an Association-Free Nonlinear Filter, Digital Signal Processing 12 (2002) 484–493 Previous nonlinear filtering research has shown that by directly estimating the probability density of a target state using a track-before-detect scheme, weak and densely spaced targets can be tracked, and data association (in which reports are associated with tracks) can be avoided. Data association imposes a heavy burden on tracking, 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, a concern exists that data association is essential for estimating and correcting additive sensor biases, which are nearly always present. This paper demonstrates that target tracks and sensor biases can be estimated simultaneously using association-free nonlinear methods. We begin by defining a state consisting of target locations and a slowly drifting sensor bias. Stochastic models for state dynamics and for the measurement function are presented. A track-before-detect nonlinear filter is constructed to estimate the joint density of the state variables. A simulation that emulates estimator behavior is exercised under low signal-to-noise conditions. Simulation results are presented and discussed. This work extends the useful range of nonlinear filtering methods in tracking applications.

Track and bias estimation without data association

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.

Robust SAR ATR by hedging against uncertainty

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.

Multiple-frame multiple-hypothesis method for tracking at low SNR

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.

Radar signature generation for feature-aided tracking research

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 Sensors 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.

Unified generalized Bayesian accrual of evidence for robust ATR: new results

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.