Multitarget detection and acquisition: A unified approach

被引:4
作者
Mahler, R [1 ]
O'Hely, M [1 ]
机构
[1] Lockheed Martin Tact Def Syst, Eagan, MN USA
来源
SIGNAL AND DATA PROCESSING OF SMALL TARGETS 1999 | 1999年 / 3809卷
关键词
data fusion; multitarget tracking; detection; acquisition; random sets;
D O I
10.1117/12.364023
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Suppose that a multitarget tracker is used to track a dim target in heavy clutter. In the paper "Characteristics of the Acquisition of a Dim Target in Clutter," Oliver Drummond has pointed out that, for such a tracker, target acquisition (i.e., determining whether or not a target is present in a sensor Field of View corrupted by persistent clutter tracks) is a very different problem than target detection (i.e., determining whether or not a target is present in a pixel corrupted by noise). In particular, target acquisition requires two Receiver Operating Characteristic (ROC) curves; whereas target target detection requires only one. In past presentations at this and other conferences and in the book Mathematics of Data Fusion, we have introduced "finite-set statistics (FISST)", a direct generalization of conventional single-sensor, single-target statistics to the multitarget realm. In this paper we show how FISST results in a unification of both detection and acquisition under a familiar decision-theoretic framework. The basic idea is to use a unified Bayesian single-target tracker with clutter models, rather than a multitarget tracker. In analogy to a conventional detection problem (i.e., deciding between the hypotheses "noise only" versus "target + noise"), the acquisition problem is reduced to a conventional decision problem (i.e., deciding between the hypotheses "clutter only" versus "target + clutter"). In particular, we show how to define an "acquisition ROC curve" for this sort of generalized detection problem.
引用
收藏
页码:218 / 229
页数:12
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