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XIONG Junlong, WANG Zhen, HUANG Yuchen, et al. Adaptive multifocus correlation filter with bayesian fusion for maritime radar target tracking[J]. Journal of Radars, in press. doi: 10.12000/JR25106
Citation: XIONG Junlong, WANG Zhen, HUANG Yuchen, et al. Adaptive multifocus correlation filter with bayesian fusion for maritime radar target tracking[J]. Journal of Radars, in press. doi: 10.12000/JR25106
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Adaptive Multifocus Correlation Filter with Bayesian Fusion for Maritime Radar Target Tracking

DOI: 10.12000/JR25106 CSTR: 32380.14.JR25106

  • School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China

  • Received Date: 2025-06-05
  • Rev Recd Date: 2024-11-08
    • Available Online: 2025-11-08
    Abstract
  • This study addresses the critical challenge of radar target tracking in complex maritime environments. Although conventional feature-aided Bayesian tracking methods have advanced in maritime radar applications, their robustness considerably deteriorates in scenarios with severe sea clutter and interacting targets. To overcome these limitations, an adaptive multifocus correlation filter with Bayesian fusion (AMFCF-BF) is proposed herein. The method constructs multiple subviews within the probabilistic distribution of the target state, with each subview assigned an independent correlation filter to generate a local response map, enabling multihypothesis state modeling. During iterative tracking, these response maps are used to estimate states and dynamically guide the focusing of subviews toward high-confidence regions, enhancing adaptability to complex target maneuvers. To further mitigate false alarms and missed detections caused by strong sea clutter, a virtual-view simulation based focusing model is developed, which effectively suppresses filter drift under adverse conditions. Finally, all subview responses are fused within a Bayesian multimeasurement framework to produce a globally consistent target-state estimate. Experimental results using simulated and real maritime radar data demonstrate that the proposed AMFCF-BF achieves an average center location error of 3.47 pixels, reducing tracking error by ～70% compared with typical feature-assisted correlation filtering methods. In terms of location precision, the proposed filter achieves an overall improvement of ～21%, showing significantly enhanced tracking accuracy and anti-interference performance, validating the effectiveness of the multifocus correlation filtering mechanism and Bayesian fusion strategy.

     

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