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

摘要

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).