John R. Hoffman

Multitarget miss distance via optimal assignment

The concept of miss distance-Euclidean, Mahalanobis, etc.-is a fundamental, far-reaching, and taken-for-granted element of the engineering theory and practice of single-target systems. In this paper we introduce a comprehensive L/sub p/-type theory of distance metrics for multitarget (and, more generally, multiobject) systems. We show that this theory extends, and provides a rigorous theoretical basis for, an intuitively appealing optimal-assignment approach proposed by Drummond for evaluating the performance of multitarget tracking algorithms. We describe tractable computational approaches for computing such metrics based on standard optimal assignment or convex optimization techniques. We describe the potentially far-reaching implications of these metrics for applications such as performance evaluation and sensor management. In the former case, we demonstrate the application of multitarget miss-distance metrics as measures of effectiveness (MoEs) for multitarget tracking algorithms.

Multitarget miss distance and its applications

The concept of miss distance-Euclidean, Mahalanobis, etc.-is a fundamental, far-reaching, and taken-for-granted element of the engineering theory and practice of single-sensor, single-target systems. One might expect that multisensor, multitarget information fusion theory and applications would already rest upon a similarly fundamental concept-namely, miss distance between multi-object systems (i.e., systems in which not only individual objects can vary, but their number as well). However, this has not been the case. Consequently, in this paper we introduce a comprehensive theory of distance metrics for multitarget (and, more generally, multi-object) systems. We show that this theory extends an optimal-assignment approach proposed by O. Drummond. We describe tractable computational approaches for computing such metrics, as well as some potentially far-reaching implications for applications such as sensor management.

Scientific performance evaluation for distributed sensor management and adaptive data fusion

For the last two years at this conference, we have described the implementation of a unified, scientific approach to performance measurement for data fusion algorithms based on FINITE-SET STATISTICS (FISST). FISST makes it possible to directly extend Shannon-type information metrics to multisource, multitarget problems. In previous papers we described application of information Measures of Effectiveness (MoEs) to multisource-multitarget data fusion and to non-distributed sensor management. In this follow-on paper we show how to generalize this work to DISTRIBUTED sensor management and ADAPTIVE DATA FUSION.

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.

User-defined information and scientific performance evaluation

For the past two years at this conference we have described results in the practical implementation of a unified, scientific approach to performance measurement for data fusion algorithms. Our approach is based on finite set statistics (FISST), a generalization of conventional statistics to multisource, multitarget problems. Finite-set statistics makes it possible to directly extend Shannon-type information metrics to multisource, multitarget problems in such a way that information can be defined and measured even though any given end-user may have conflicting or even subjective definitions of what information means. In this follow-on paper we describe the performance of FISST based metrics that take into account a users definition of information and develop a rigorous theory of partial information for multisource, multi-target problems.

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.

Scientific performance estimation of robustness and threat

For the last three years at this conference we have been describing the implementation of a unified, scientific approach to performance estimation for various aspects of data fusion: multitarget detection, tracking, and identification algorithms; sensor management algorithms; and adaptive data fusion algorithms. The proposed approach is based on finite-set statistics (FISST), a generalization of conventional statistics to multisource, multitarget problems. Finite-set statistics makes it possible to directly extend Shannon-type information metrics to multisource, multitarget problems in such a way that information can be defined and measured even though any given end-user may have conflicting or even subjective definitions of what informative means. In this presentation, we will show how to extend our previous results to two new problems. First, that of evaluating the robustness of multisensor, multitarget algorithms. Second, that of evaluating the performance of multisource-multitarget threat assessment algorithms.

Practical application of a branching particle-based nonlinear filter

Particle-based nonlinear filters provide a mathematically optimal (in the limit) and sound method for solving a number of difficult filtering problems. However, there are a number of practical difficulties that can occur when applying particle-based filtering techniques to real world problems. These problems include highly directed signal dynamics highly definitive observations clipped observation data. Current approaches to solving these problems generally require increasing the number of particles, but to obtain a given level of performance the number of particles required may be extremely large. We propose a number of techniques to ameliorate these difficulties. We adopt the ideas of simulated annealing and add noise which is damped in time to the particle states when they are evolved or duplicated, and also add noise which is damped in time to the interpretation of the observations by the filter, to deal with signal dynamics and observation problems. We modify the method by which particles are duplicated to deal with different information flows into the system depending on the location of the particle and the information flow into the particle. We discuss the success we have had with these solutions on some of the problems of interest to Lockheed Martin and the MITACS-PINTS research center.

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.