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Combined Adaptive Normalized Matched Filter Detection of Moving Target in Sea Clutter

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Abstract

In this paper, combined adaptive normalized matched filter (ANMF) detector is proposed to detect moving target in sea clutter. For the long integration time, moving target suffers from Doppler frequency changes in individual range cells, and the sea clutter is nonstationary along the pulse dimension. Therefore, ANMF detector is ineffective in this situation. In order to solve this problem, combined ANMF detector is proposed to detect moving target in sea clutter. Firstly, the long integration duration is segmented into some short subintervals. In each subinterval, the normalized Doppler frequency of the moving target is assumed to be a constant. Secondly, a series of ANMF detectors with different normalized Doppler frequencies are used to obtain a train of coherent integration results in each subinterval, which constitute the ANMF output vector. Finally, we use the multiply integration (MI) to integrate the ANMFOVs from different subintervals. The test statistic, the max-value of MI along the normalized Doppler frequency dimension, is used for the proposed combined ANMF detector. The real sea clutter and simulated clutter data are used to evaluate the proposed detector, and the experimental results show that the proposed detector achieves better detection performance than the conventional ANMF detector.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (61201296, 61271024 and 61372136), the Fundamental Research Funds for the Central Universities (JB160224) and Young Talent fund of University Association for Science and Technology in Shaanxi (20160205). The authors wish to thank the associate editor and the reviewers, who provided insightful comments and constructive criticisms that greatly improved the manuscript.

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Correspondence to Shu-Wen Xu.

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Xu, SW., Shui, PL., Yan, XY. et al. Combined Adaptive Normalized Matched Filter Detection of Moving Target in Sea Clutter. Circuits Syst Signal Process 36, 2360–2383 (2017). https://doi.org/10.1007/s00034-016-0413-5

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  • DOI: https://doi.org/10.1007/s00034-016-0413-5

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