Abstract
Knowledge of the clutter rate is of critical importance in multi-target Bayesian tracking. However, estimating the clutter rate is a difficult problem in practice. In this paper, an improved multi-Bernoulli filter based on random finite sets for multi-target Bayesian tracking accommodating non-linear dynamic and measurement models, as well as unknown clutter rate, is proposed for radar sensors. The proposed filter incorporates the amplitude information into the state and measurement spaces to improve discrimination between actual targets and clutters, while adaptively generating the new-born object random finite sets using the measurements to eliminate reliance on prior random finite sets. A sequential Monte-Carlo implementation of the proposed filter is presented, and simulations are used to demonstrate the proposed filter’s improvements in estimation accuracy of the target number and corresponding multi-target states, as well as the clutter rate.
Highlights
As a system, the radar has been widely applied in both civil and military areas due to its all-weather, day and night capability compared with optical and infrared sensors [1]
We evaluate the performance of the proposed UCR-multi-Bernoulli filter (MBerF)-AI with a fixed clutter rate
Compared with the true trajectories, the results indicate that the UCR-MBerF-AI can correctly determine actual target appearance, motion and disappearance, and achieve accurate multi-target tracking without the need of the new-born objects’ prior random finite set (RFS)
Summary
The radar has been widely applied in both civil and military areas due to its all-weather, day and night capability compared with optical and infrared sensors [1]. Multi-target tracking with unknown clutter rate has been addressed with methods based on PHD and CPHD filters [14,15]. ‚ We carry out the SMC implementation of the proposed filter for general non-linear multi-target tracking scenarios. 2. RFS Model with Amplitude Information for Radar Sensors under Unknown Clutter Rate. We show how amplitude information is incorporated into an RFS model that accommodates unknown clutter rate scenarios. We present the likelihood functions for the clutter and actual target by adopting radar amplitude models
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