Regional Variance for Multi-Object Filtering
Regional Variance for Multi-Object Filtering
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DOI：
10.1109/tsp.2014.2328326
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发表时间：
2013-10
影响因子：
5.4
通讯作者：
E. Delande;Murat Üney;J. Houssineau;Daniel E. Clark
中科院分区：
文献类型：
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作者：
E. Delande;Murat Üney;J. Houssineau;Daniel E. Clark
多目标滤波的最新进展导致了基于传感器测量计算多目标分布一阶矩的算法。利用一阶矩估计任意选取区域内的目标数量。本文引入了目标数二阶统计量的显式计算公式。提出的区域方差概念量化了任意区域目标数估计的置信度水平，促进了基于信息的决策。给出了概率假设密度（PHD）和基数概率假设密度（CPHD）滤波器的计算算法。我们通过模拟实例证明了区域统计的行为。
Recent progress in multi-object filtering has led to algorithms that compute the first-order moment of multi-object distributions based on sensor measurements. The number of targets in arbitrarily selected regions can be estimated using the first-order moment. In this work, we introduce explicit formulae for the computation of the second-order statistic on the target number. The proposed concept of regional variance quantifies the level of confidence on target number estimates in arbitrary regions and facilitates information-based decisions. We provide algorithms for its computation for the probability hypothesis density (PHD) and the cardinalized probability hypothesis density (CPHD) filters. We demonstrate the behaviour of the regional statistics through simulation examples.