Multiple Model CPHD Filter for Tracking Maneuvering Targets

Jie Sun; Dong Li

doi:10.4028/www.scientific.net/AMM.556-562.3238

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An Engineering Approach for Observer Trajectory Optimization in Passive Target Tracking System
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Positioning Method without Current Navigation Data
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Multiple Model CPHD Filter for Tracking Maneuvering Targets
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Monitoring and Control System Design for Tool Wear Condition of CNC Machine Based on Artificial Neural Networks
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A FBG-Based Robust Damage Identification Approach Considering Environmental and Operational Conditions Effects
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 556-562Multiple Model CPHD Filter for Tracking...

Multiple Model CPHD Filter for Tracking Maneuvering Targets

Abstract:

A multiple model cardinalized probability hypothesis density (CPHD) filter is proposed for tracking multiple maneuvering targets. The augmented state is established by combining the target motion mode with the kinematic state. Both the posterior cardinality distribution of the targets and the posterior probability hypothesis density (PHD) of the augmented state are jointly propagated by using CPHD recursion. Simulation results show that the proposed filter improves the estimation accuracy of target number and target states over the multiple model PHD filter and single model CPHD filter respectively.