Abstract
The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter developed recently has been proved an effective multi-target tracking (MTT) algorithm based on the random finite set (RFS) theory, and it can jointly estimate the number of targets and their states from a sequence of sensor measurement sets. However, because of the existence of systematic errors in sensor measurements, the CBMeMBer filter can easily produce different levels of performance degradation. In this paper, an extended CBMeMBer filter, in which the joint probability density function of target state and systematic error is recursively estimated, is proposed to address the MTT problem based on the sensor measurements with systematic errors. In addition, an analytic implementation of the extended CBMeMBer filter is also presented for linear Gaussian models. Simulation results confirm that the proposed algorithm can track multiple targets with better performance.
Highlights
The random finite set (RFS) theory [1] has provided an elegant formulation for the multi-target tracking (MTT) problem and has already gained substantial interest
The probability hypothesis density (PHD) multi-target filter [2] is an effective approach for tracking multiple targets based on the RFS theory, as it can simultaneously estimate the number and the state of targets without the measurement-to-track association used in the traditional MTT approaches [3,4,5,6]
One is known as the sequential Monte Carlo (SMC)-PHD filter or particle PHD filter [7,8] and the other is known as the Gaussian mixture (GM)-PHD filter [9,10]
Summary
The random finite set (RFS) theory [1] has provided an elegant formulation for the multi-target tracking (MTT) problem and has already gained substantial interest. The probability hypothesis density (PHD) multi-target filter [2] is an effective approach for tracking multiple targets based on the RFS theory, as it can simultaneously estimate the number and the state of targets without the measurement-to-track association used in the traditional MTT approaches [3,4,5,6]. By introducing the joint probability density function of the target state and systematic error, the proposed filter can be derived from modifying the CBMeMBer recursion equations directly. The analytic implementation of the extended CBMeMBer filter is derived by using the bias measurement models and the linear Gaussian assumptions on target models. Simulation results verify that the proposed algorithm outperforms GM-CBMeMBer filter in both the aspects of target state estimation and target number estimation by using the biased measurements.
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