Whitening Degree Evaluation Method to Test Estimate Accuracy of Speckle Covariance Matrix

Yu Han; Shui Penglang; Yang Chunjiao; Shi Sainan

doi:10.12000/JR16146

Volume 6 Issue 3

Jun. 2017

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Article Navigation > Journal of Radars > 2017 > 6(3): 285-291

YU Wenxian. Automatic target recognition from an engineering perspective[J]. Journal of Radars, 2022, 11(5): 737–752. doi: 10.12000/JR22178

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Yu Han, Shui Penglang, Yang Chunjiao, Shi Sainan. Whitening Degree Evaluation Method to Test Estimate Accuracy of Speckle Covariance Matrix[J]. Journal of Radars, 2017, 6(3): 285-291. doi: 10.12000/JR16146

YU Wenxian. Automatic target recognition from an engineering perspective[J]. Journal of Radars, 2022, 11(5): 737–752. doi: 10.12000/JR22178

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Whitening Degree Evaluation Method to Test Estimate Accuracy of Speckle Covariance Matrix

DOI: 10.12000/JR16146

National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (61671357)

Received Date: 2016-12-16
Rev Recd Date: 2017-04-24

Available Online: 2017-05-27

Publish Date: 2017-06-28

Abstract

Abstract

In the background of sea clutter, the accuracy of adaptive target detection is heavily influenced by the estimated performance of speckle covariance matrix. Generally, Normalized Frobenius Norm (NFN) is used to test the estimated accuracy of different speckle covariance matrix estimators, in which the requirement of a known real covariance matrix is hardly realized in the radar system. Therefore, in this study, a whitening degree evaluation method is proposed wherein the decorrelation of speckle covariance matrix in whitening filter processing of the radar system is fully exploited. It considers the correlation degree among pulses in the whitening clutter vector as the criterion to evaluate the estimate error of the speckle covariance matrix. The proposed method shows consistent conclusions with NFN on simulated data and also avoids limitations of the latter method in real data processing.
- Covariance matrix estimation,
- Whitening degree evaluation,
- Normalized Frobenius Norm (NFN),
- Real covariance matrix

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References(18)

References

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