Abstract
In multi-target tracking, the outliers-corrupted process and measurement noises can reduce the performance of the probability hypothesis density (PHD) filter severely. To solve the problem, this paper proposed a novel PHD filter, called Student’s t mixture PHD (STM-PHD) filter. The proposed filter models the heavy-tailed process noise and measurement noise as a Student’s t distribution as well as approximates the multi-target intensity as a mixture of Student’s t components to be propagated in time. Then, a closed PHD recursion is obtained based on Student’s t approximation. Our approach can make full use of the heavy-tailed characteristic of a Student’s t distribution to handle the situations with heavy-tailed process and the measurement noises. The simulation results verify that the proposed filter can overcome the negative effect generated by outliers and maintain a good tracking accuracy in the simultaneous presence of process and measurement outliers.
Highlights
Multi-target tracking (MTT) plays an important role in many sensing systems, such as infrared, radar, sonar, etc., which uses the sensor data to jointly estimate the target state and the number of targets.Nowadays it is widely used in civilian and military applications such as air traffic control, remote sensing, ballistic missile guidance, and computer vision [1,2]
The random finite set (RFS) theory-based multi-target tracking filters, such as probability hypothesis density (PHD) filter [3], cardinalized PHD (CPHD) filter [4], multi-target multi-Bernoulli (MeMber) filter [1] and cardinality-balanced MeMBer (CBMeMBer) filter [5], have attracted much more attention since they can avoid the combinatorial problem that arises from data association
To illustrate the performance of the proposed filter, simulation examples are designed to compare with standard Gaussian mixture (GM)-PHD filter in linear and nonlinear scenarios, respectively
Summary
Multi-target tracking (MTT) plays an important role in many sensing systems, such as infrared, radar, sonar, etc., which uses the sensor data to jointly estimate the target state and the number of targets. The two methods above do not change the foundation of the Gaussian approximation-based GM-PHD filter This means that the noise model still cannot match the outliers-corrupted process and measurement noises well, leading to biased estimates of the target state and the number of targets. A novel implementation of the PHD filter is proposed based on Student’s t mixture approximation, intending to improve the estimation accuracy in terms of the target states and the target number in the presence of heavy-tailed process and measurement noises. Compared to the GM case, it is a Student’s t-based implementation, which propagates a mixture of Student’s t components Because it utilizes the heavy-tailed characteristic of Student’s t distribution, the proposed filter has better accuracy and robustness in MTT scenes with heavy-tailed process and measurement noises.
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