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Maximum-Eigenvalue-Based Multiframe Track-Before-Detect Method for Weak Moving Target Detection
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AI
DOI：10.1109/TAES.2025.3543153.png)
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Abstract
En 中文
To address the problem of weak moving target detection, this article proposes a maximum eigenvalue (ME)-based multiframe track-before-detect (TBD) method to implement multiframe integration and enhance target detection performance. Specifically, the MEs of Hermitian positive-definite matrices for the intraframe radar echo signals are used to form an ME detector, and the performance of the detector is guaranteed by the generalized likelihood ratio test. Then, to integrate interframe target information, we apply an efficient dynamic programming (DP) algorithm, for which the scoring function is derived by designing an ME-based multitask optimization scheme. As a consequence, an ME-based DP-TBD method is developed, which does not rely on any prior knowledge about the target and the clutter. The advantages of the proposed method are validated through experiments utilizing both simulated data and real radar data. The results show that the proposed method obtains better performance in comparison with the state-of-the-art methods.
Keywords:
Dynamic programming (DP)
maximum eigenvalue (ME)
radar detection
track before detect (TBD)
weak moving target
Journal
IF:
5.7
Papers:
863
Citations:
2.4W
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