Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Statistical Multisource-Multitarget Information Fusion
Statistical Multisource-Multitarget Information Fusion
The cardinality balanced multi-target multi-Bernoulli filter and its implementations
IEEE Transactions on Signal Processing
Convergence results for the particle PHD filter
IEEE Transactions on Signal Processing
The Gaussian Mixture Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing
Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing - Part II
A Consistent Metric for Performance Evaluation of Multi-Object Filters
IEEE Transactions on Signal Processing - Part I
CPHD Filtering With Unknown Clutter Rate and Detection Profile
IEEE Transactions on Signal Processing
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Random finite set (RFS) filters have been demonstrating a promising algorithm for tracking an unknown number of targets in real time. However, these methods can only be used in the multi-target tracking systems with known measurement noise variances; otherwise, their tracking performances will decline greatly. To solve this problem, an improved multi-target tracking algorithm is proposed based on the cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filter and the variational Bayesian (VB) approximation technique to recursively estimate the joint posterior distributions of the multi-target states and the time-varying measurement noise variances. First, the variational calculus method is employed to derive the multi-target estimate recursions, and then the Gaussian and inverse Gamma mixture distributions are introduced to approximate the joint posterior density, and achieve a Gaussian closed-form solution. Simulation results show that the proposed algorithm can effectively estimate the unknown measurement noise variances and has a good performance of multi-target tracking with a strong robustness.