Detecting, tracking, and classifying group targets: a unified approach

Ronald P. S. Mahler

doi:10.1117/12.436950

16 August 2001 Detecting, tracking, and classifying group targets: a unified approach

Ronald P. S. Mahler

Proceedings Volume 4380, Signal Processing, Sensor Fusion, and Target Recognition X; (2001) https://doi.org/10.1117/12.436950
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

A group target is a collection of individual targets that are part of some larger military formation such as a brigade, tank column, aircraft carrier group, etc. Unlike conventional targets, group targets are fuzzy in the sense that it is not possible to precisely define their identities in actual battlefield situations. It is also not necessarily possible to detect (let alone track or identify) each and every platform in a given group. Force aggregation (also known as situation assessment or Level 2 data fusion) is the process of detecting, tracking, and identifying group targets. A suitable generalization of the Bayes recursive filter is the theoretically optimal basis for detection, tracking, and identification of multiple targets using multiple sensors. However, it is not obvious what filtering even means in the context of group targets. In this paper we present a theoretically unified, rigorous, and potentially practical approach to force aggregation. Using finite-set statistics (FISST), I show how to construct a theoretically optimal recursive Bayes filter for the multisensor-multigroup problem. Potential computational tractability is achieved by generalizing the concept of a probability hypothesis density (PHD).

Citation Download Citation

Ronald P. S. Mahler "Detecting, tracking, and classifying group targets: a unified approach", Proc. SPIE 4380, Signal Processing, Sensor Fusion, and Target Recognition X, (16 August 2001); https://doi.org/10.1117/12.436950

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