Regional variance for multi-object filtering

2013/10/10 by Emmanuel Delande, Murat Üney, Delande, Emmanuel +5
Computer Science · Engineering · #Target Tracking and Data Fusion in Sensor Networks #Distributed Sensor Networks and Detection Algorithms #Water Systems and Optimization

paper · pdf · doi:10.48550/arxiv.1310.2873

Abstract

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.

Citations

• Sequential monte carlo methods for multi-target filtering with random finite sets

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