Abstract
Assuming that the measurement and process noise covariances are known, the probability hypothesis density (PHD) filter is effective in real-time multi-target tracking; however, noise covariance is often unknown and time-varying for an actual scene. To solve this problem, a strong tracking PHD filter based on Variational Bayes (VB) approximation is proposed in this paper. The measurement noise covariance is described in the linear system by the inverse Wishart (IW) distribution. Then, the fading factor in the strong tracking principle uses the optimal measurement noise covariance at the previous moment to control the state prediction covariance in real-time. The Gaussian IW (GIW) joint distribution adopts the VB approximation to jointly return the measurement noise covariance and the target state covariance. The simulation results show that, compared with the traditional Gaussian mixture PHD (GM-PHD) and the VB-adaptive PHD, the proposed algorithm has higher tracking accuracy and stronger robustness in a more reasonable calculation time.
Highlights
Multi-target tracking is one of the core issues in information fusion research, which has been widely used in civil and military fields such as aerospace, electronic information, and control engineering
Based on the above research, this paper proposes the strong tracking probability hypothesis density (PHD)-based on a Variational Bayes (VB) filter in the case of the inaccurate process noise covariance and measurement noise covariance slowly time-varying
This paper introduces the principle of strong tracking on the basis of the VB adaptive PHD filter and the Gaussian IW (GIW) implementation of the VB-based strong tracking PHD filter is derived for linear multi-target uncertain systems
Summary
Multi-target tracking is one of the core issues in information fusion research, which has been widely used in civil and military fields such as aerospace, electronic information, and control engineering. It mainly estimates the number and state of the target through the data obtained by sensors, where the target is possible to be born, die, and derive at any time. As the number of targets increases, the calculation amount of these algorithms increases exponentially, which seriously affects the real-time performance.
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