A Robust Clutter Edge Detection Method Based on Model Order Selection Criterion

JIN Yuxi; WU Min; HAO Chengpeng; YIN Chaoran; WU Yongqing; YAN Linjie

doi:10.11999/JEIT230999

Article Contents

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JIN Yuxi, WU Min, HAO Chengpeng, YIN Chaoran, WU Yongqing, YAN Linjie. A Robust Clutter Edge Detection Method Based on Model Order Selection Criterion[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230999

Citation:

JIN Yuxi, WU Min, HAO Chengpeng, YIN Chaoran, WU Yongqing, YAN Linjie. A Robust Clutter Edge Detection Method Based on Model Order Selection Criterion[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230999

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A Robust Clutter Edge Detection Method Based on Model Order Selection Criterion

doi: 10.11999/JEIT230999

1.
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences , Beijing 100049, China

Funds: The National Natural Science Foundation (62001468, 61971412, 62071460, 62371446, 62201564)

Received Date: 2023-09-13
Rev Recd Date: 2024-01-17

Available Online: 2024-02-04

Abstract

Abstract

In the radar target adaptive detection problem, the presence of clutter edges in the auxiliary data will cause a serious decrease in the estimation performance of the Clutter Covariance Matrix (CCM), which greatly affects the target detection performance. In order to solve this problem, a clutter edge detection method is proposed, which can adaptively discriminate the number and position of clutter edges in auxiliary data. Firstly, assuming the presence of clutter edges in the auxiliary data, the model order selection algorithm and the maximum likelihood estimation method are used to complete the clutter parameter estimation, and the clutter edge position is obtained by the cyclic search method. Then, the clutter parameter estimation results are applied to the detection algorithm, and the existence of clutter edges is determined by the generalized likelihood ratio test method. In addition, in order to further improve the robustness of the algorithm under the condition of small samples, the special structure of CCM is introduced as a priori knowledge, and the algorithm is generalized to the situation where CCM is persymmetry, spectrum symmetry and central-symmetry. Both simulation and measured data show that the proposed algorithm can efficiently identify the number and location of clutter edges in radar auxiliary data, and the introduction of prior knowledge can further improve the performance of the algorithm when the amount of auxiliary data is small.
- Adaptive radar detection,
- Clutter classification,
- Clutter edge,
- Knowledge-based

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References(25)

References

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