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SCIENTIA SINICA Informationis, Volume 47 , Issue 2 : 247-259(2017) https://doi.org/10.1360/N112016-00105

Improved matrix CFAR detector based on K-L divergence and divergence mean}{Improved matrix CFAR detector based on K-L divergence and divergence mean

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  • ReceivedJul 3, 2016
  • AcceptedSep 30, 2016
  • PublishedDec 16, 2016

Abstract

The target detection of traditional radar, which is usually realized by fast Fourier transforms (FFT) associated with a constant false alarm rate detector, faces the following problems: (1) The Doppler resolution of the FFT is low when the measurement length is short, resulting in Doppler bestriding loss. (2) The broadening of the spectrum causes the clutter energy to disperse toward the target. (3)To the target extended in frequency domain, the single channel detection will lose much energy of the target measures. An improved matrix CFAR detector is proposed, which uses the Kullback-Leibler divergence and divergence mean to replace the geodesic distance and Riemannian mean to overcome the three above-mentioned problems. Its CFAR performance and detection are better than that of the basic matrix CFAR detector; the calculated amount is simultaneously reduced. Finally, the performance of the improved matrix CFAR detector is analyzed by simulation experiments. The results indicate a good performance.


Funded by

国家自然科学基金(61179014)

国家自然科学基金(61302193)


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