Key Takeaways & Executive Findings
- •• The proposed PSVC-MB filter significantly improves tracking accuracy for maritime dim targets under low signal-to-clutter ratios. • Replacing resampling with particle position scaling effectively mitigates particle impoverishment and preserves particle diversity. • Velocity correction using pseudo point measurements from multi-frame information reduces velocity uncertainty and prevents tracking loss. • Simulation results demonstrate the superior performance of PSVC-MB compared to the standard SMC-MB-TBD method at various low SCRs.
Abstract
For the problem of tracking maritime dim targets, the sequential Monte–Carlo multi-Bernoulli track-before-detect (SMC-MB-TBD) method is popular. However, this method may face low tracking accuracy and tracking loss due to particle impoverishment and velocity uncertainty. In this study, a novel filter called position scaling and velocity correction multi-Bernoulli (PSVC-MB) is proposed to deal with this problem. First, particle position scaling is used to replace resampling in the SMC-MB-TBD method to deal with the lack of particle diversity. Second, when the target is stably tracked, the target velocity is extracted from the multi-frame information and used for re-estimation. Pseudo point measurements are calculated from the weighted average of all locations near the particle position, and the particle velocity will be continuously corrected with the pseudo point measurements. Simulation results verify the effectiveness of the proposed method at different low signal-to-clutter ratios (SCRs).
1. Introduction
Target tracking is usually carried out after setting detection thresholds and compressing observation data into a finite set of point measurements. However, this method may not be suitable in low signal-to-clutter ratio (SCR) situations. A high detection threshold will lead to low detection probability and low yields. Consequently, it is important to use all the information containing the original observation data to improve the tracking performance (Kim et al., 2019).
It is a challenging problem to detect and track dim targets with maritime radar in a complex marine environment. When the radar receives an echo signal generated by the targets, the echo signal will experience interference from the sea clutter, which is reflected from the sea surface (Xu et al., 2021). Sea clutter is not only related to wind speed and surge of the sea surface, but also affected by many factors such as radar parameters and radar operating modes (Posner, 2002). In the case of high resolution, the amplitude of sea clutter has more significant heavy-tail characteristics, and a reasonable sea clutter echo model is K-distribution (Ward et al., 2013).
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Liwei Shi, Yunfei Guo, Wenxiong Cui, Yanbo Xue, Yun Chen (2025). An Efficient Multi-Bernoulli Filter for Tracking Multiple Maritime Dim Targets. Frontiers of Information Technology & Electronic Engineering. https://doi.org/10.1631/FITEE_2400449
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Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes an efficient multi-Bernoulli filter named PSVC-MB that combines particle position scaling and velocity correction to improve tracking accuracy and reduce tracking loss for maritime dim targets under low signal-to-clutter ratios.
What problem does the SMC-MB-TBD method face?
The SMC-MB-TBD method suffers from particle impoverishment and velocity uncertainty, which can lead to low tracking accuracy and tracking loss when tracking maritime dim targets.
How does the proposed filter improve particle diversity?
The proposed filter replaces resampling with particle position scaling, which mitigates sample impoverishment and preserves particle diversity during the filtering process.
How is target velocity estimated and corrected in the PSVC-MB filter?
Target velocity is extracted from multi-frame information and continuously corrected using pseudo point measurements, which are calculated from the weighted average of all locations near the particle position.
What do the simulation results show?
Simulation results verify the effectiveness of the proposed method at different low signal-to-clutter ratios, demonstrating improved tracking accuracy and reduced tracking loss compared to the standard SMC-MB-TBD algorithm.
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