不完全测量条件下纯方位机动目标跟踪的Cramer-Rao下界

X. Zhigang, Sheng Andong, L. Yinya
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引用次数: 2

摘要

在观测值随机丢失的情况下,导出了纯方位机动目标跟踪的理论Cramer-Rao下界(CRLB)。引入两个二元变量分别对两个事件建模,一个是目标机动或不机动,另一个是目标被探测或未探测到。然后,在一般非线性系统的序贯式随机负载均衡的基础上,推导出相应的理论随机负载均衡递推公式。理论公式在检测概率小于1的情况下,费雪信息矩阵(FIM)的计算量较大。提出了理论界的近似。此外,提出了一个检测降低因子界,并证明其小于理论的CRLB。最后用数值算例说明了计算结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cramer-Rao lower bounds for bearings-only maneuvering target tracking with incomplete measurements
The theoretical Cramer-Rao lower bound (CRLB) for bearings-only maneuvering target tracking is derived in the case where the observation measurements are lost in a random fashion. Two binary variables are introduced to model two events respectively, one which the target maneuvers or not and another that the target is detected or missed. The corresponding recursive formula for theoretical CRLB is then derived based on the sequential version of the CRLB for general nonlinear systems. The theoretical formula suffers from heavy calculation load of the Fisher information matrix (FIM) while the constant probability of detection is less than unity. An approximation of the theoretical bound is proposed. In addition, a detection reduction factor bound is presented and proved to be less than the theoretical CRLB. The results are illustrated with a numerical example.
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