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SCIENCE CHINA Information Sciences, Volume 60, Issue 11: 112302(2017) https://doi.org/10.1007/s11432-016-9105-7

Multichannel radar adaptive signal detection in interference and structure nonhomogeneity

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  • ReceivedNov 16, 2016
  • AcceptedApr 28, 2017
  • PublishedSep 19, 2017

Abstract

In this paper, we consider the problem of multichannel radar signal detection in interference and structure nonhomogeneity. The interference is often caused by electromagnetic countermeasure (ECM) systems or industrial activity, while the nonhomogeneity usually arises because of rapid variations in terrain or radar antenna structure. We propose three adaptive detectors according to three common criteria of detector design, namely, the generalized likelihood ratio test (GLRT), Rao test, and Wald test. Extensive performance comparisons are conducted under different scenarios. It is shown that when the nonhomogeneity is severe, the detector devised according to the GLRT achieves the best detection performance. In other scenarios, the detector designed according to the Wald test may be the best choice, which has the highest probability of detection.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61501505, 61501351).


References

[1] Kelly E J. An adaptive detection algorithm. IEEE Trans Aerospace Electron Syst 1986, 22: 115--127. Google Scholar

[2] Chen W S, Reed I S. A new CFAR detection test for radar. Digital Signal Process 1991, 1: 198--214. Google Scholar

[3] Robey F C, Fuhrmann D R, Kelly E J, et al. A CFAR adaptive matched filter detector. IEEE Trans Aerospace Electron Syst 1992, 28: 208--216. Google Scholar

[4] de Maio A. A new derivation of the adaptive matched filter. IEEE Signal Process Lett 2004, 11: 792--793. Google Scholar

[5] de Maio A. Rao test for adaptive detection in Gaussian interference with unknown covariance matrix. IEEE Trans Signal Process 2007, 55: 3577--3584. Google Scholar

[6] Pastina D, Lombardo P, Bucciarelli T. Adaptive polarimetric target detection with coherent radar part I: detection against Gaussian background. IEEE Trans Aerospace Electron Syst 2001, 37: 1194--1206. Google Scholar

[7] Liu J, Zhang Z J, Yang Y. Optimal waveform design for generalized likelihood ratio and adaptive matched filter detectors using a diversely polarized antenna. Signal Process 2012, 92: 1126--1131. Google Scholar

[8] Liu W J, Xie W C, Liu J, et al. Adaptive double subspace signal detection in Gaussian background---part I: homogeneous environments. IEEE Trans Signal Process 2014, 62: 2345--2357. Google Scholar

[9] Xu J, Yu J, Peng Y N, et al. Radon-fourier transform for radar target detection. I: Generalized Doppler filter bank. IEEE Trans Aerospace Electron Syst 2011, 47: 1186--1202. Google Scholar

[10] Xu J, Xia X G, Peng S B, et al. Radar maneuvering target motion estimation based on generalized Radon-Fourier transform. IEEE Trans Signal Process 2012, 60: 6190--6201. Google Scholar

[11] Chen X, Guan J, Liu N B, et al. Maneuvering target detection via Radon-fractional Fourier transform-based long-time coherent integration. IEEE Trans Signal Process 2014, 62: 939--953. Google Scholar

[12] Li X L, Cui G L, Yi W, et al. A fast maneuvering target detection motion parameters estimation algorithm based on ACCF. IEEE Signal Process Lett 2015, 22: 270--274. Google Scholar

[13] Conte E, de Maio A, Ricci G. GLRT-based adaptive detection algorithms for range-spread targets. IEEE Trans Signal Process 2001, 49: 1336--1348. Google Scholar

[14] Kraut S, Scharf L L. The CFAR adaptive subspace detector is a scale-invariant GLRT IEEE Trans Signal Process 1999, 47: 2538--2541. Google Scholar

[15] de Maio A, Iommelli S. Coincidence of the Rao test, Wald test, and GLRT in partially homogeneous environment. IEEE Signal Process Lett 2008, 15: 385--388. Google Scholar

[16] Kraut S, Scharf L L. Adaptive subspace detectors. IEEE Trans Signal Process 2001, 49: 1--16. Google Scholar

[17] Liu W J, Xie W C, Liu J, et al. Adaptive double subspace signal detection in Gaussian background---part II: partially homogeneous environments. IEEE Trans Signal Process 2014, 62: 2358--2369. Google Scholar

[18] Rangaswamy M, Weiner D, Oeztuerk A. Non-Gaussian random vector identification using spherically invariant random processes. IEEE Trans Aerospace Electron Syst 1993, 29: 111--124. Google Scholar

[19] Gini F, Farina A. Vector subspace detection in compound-Gaussian clutter part I: survey and new results. IEEE Trans Aerospace Electron Syst 2002, 38: 1295--1311. Google Scholar

[20] Gini F. Sub-optimum coherent radar detection in a mixture of k-distributed and Gaussian clutter. IEEE Process 1997, 144: 39--48. Google Scholar

[21] Gerlach K. Spatially distributed target detection in non-Gaussian clutter. IEEE Trans Aerospace Electron Syst 1999, 35: 926--934. Google Scholar

[22] Cui G L, Kong L J, Yang X B, et al. Distributed target detection with polarimetric MIMO radar in compound-Gaussian clutter. Digital Signal Process 2012, 22: 430--438. Google Scholar

[23] Sangston K J, Gini F, Greco M S. Coherent radar target detection in heavy-tailed compound-Gaussian clutter. IEEE Trans Aerospace Electron Syst 2012, 64: 64--76. Google Scholar

[24] Zhang T X, Cui G L, Kong L J, et al. Phase-modulated waveform evaluation and selection strategy in compound-Gaussian clutter. IEEE Trans Signal Process 2013, 61: 1143--1148. Google Scholar

[25] Zhang T X, Cui G L, Kong L J, et al. Adaptive Bayesian detection using MIMO radar in spatially heterogeneous clutter. IEEE Signal Process Lett 2013, 20: 547--550. Google Scholar

[26] Kong L J, Li N, Cui G L, et al. Adaptive Bayesian detection for multiple-input multiple-output radar in compound-Gaussian clutter with random texture. IET Radar Sonar Navigation 2016, 10: 689--698. Google Scholar

[27] Gao Y C, Li H B, Himed B. Knowledge-aided range-spread target detection for distributed MIMO radar in nonhomogeneous environments. IEEE Trans Signal Process 2017, 65: 617--627. Google Scholar

[28] Besson O, Tourneret J Y, Bidon S. Knowledge-aided Bayesian detection in heterogeneous environments. IEEE Signal Process Lett 2007, 14: 355--358. Google Scholar

[29] Orlando D, Ricci G. A Rao test with enhanced selectivity properties in homogeneous scenarios. IEEE Trans Signal Process 2010, 58: 5385--5390. Google Scholar

[30] Besson O, Orlando D. Adaptive detection in nonhomogeneous environments using the generalized eigenrelation. IEEE Signal Process Lett 2007, 14: 731--734. Google Scholar

[31] Hao C, Orlando D, Hou C. Rao and Wald tests for nonhomogeneous scenarios. Sensors 2012, 12: 4730--4736. Google Scholar

[32] Bandiera F, Besson O, Orlando D, et al. GLRT-based direction detectors in homogeneous noise and subspace interference. IEEE Trans Signal Process 2007, 55: 2386--2394. Google Scholar

[33] Bandiera F, Besson O, Ricci G. Direction detector for distributed targets in unknown noise and interference. Electron Lett 2013, 49: 68--69. Google Scholar

[34] Liu W J, Liu J, Wang Y L, et al. Adaptive array detection in noise and completely unknown jamming. Digital Signal Process 2015, 46: 41--48. Google Scholar

[35] Liu W J, Liu J, Hu X Q, et al. Statistical performance analysis of the adaptive orthogonal rejection detector. IEEE Signal Process Lett 2016, 23: 873--877. Google Scholar

[36] Bandiera F, De Maio A, Greco A S, et al. Adaptive radar detection of distributed targets in homogeneous and partially homogeneous noise plus subspace interference. IEEE Trans Signal Process 2007, 55: 1223--1237. Google Scholar

[37] Liu W, Liu J, Huang L. Rao tests for distributed target detection in interference and noise. Signal Processing, 2015, 117: 333-342 CrossRef Google Scholar

[38] Gao Y C, Liao G S, Liu W J. High-resolution radar detection in interference and nonhomogeneous noise. IEEE Signal Process Lett 2016, 23: 1359--1363. Google Scholar

[39] Shuai X F, Kong L J, Yang J Y. Adaptive detection for distributed targets in Gaussian noise with Rao and Wald tests. Sci China Inf Sci 2012, 55: 1290--1300. Google Scholar

[40] Hao C P, Orlando D, Ma X C, et al. Persymmetric Rao and Wald tests for partially homogeneous environment. IEEE Signal Process Lett 2012, 19: 587--590. Google Scholar

[41] Liu J, Liu W J, Chen B, et al. Modified Rao test for multichannel adaptive signal detection. IEEE Trans Signal Process 2016, 64: 714--725. Google Scholar

[42] Besson O. Detection in the presence of surprise or undernulled interference. IEEE Signal Process Lett 2007, 14: 352--354. Google Scholar

[43] Yanai H, Takeuchi K, Takane Y. Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition New York: Springer, 2011. Google Scholar

[44] Liu W J, Wang Y L, Xie W C. Fisher information matrix, Rao test, and Wald test for complex-valued signals and their applications. Signal Process, 2014, 94: 1--5. Google Scholar

[45] Bandiera F, Besson O, Ricci G. An ABORT-like detector with improved mismatched signals rejection capabilities. IEEE Trans Signal Process 2007, 56: 14--25. Google Scholar

[46] Hao C P, Liu B, Cai L. Performance analysis of a two-stage Rao detector. Signal Process 2011, 91: 2141--2146. Google Scholar

[47] Liu W, Liu J, Zhang C. Performance prediction of subspace-based adaptive detectors with signal mismatch. Signal Processing, 2016, 123: 122-126 CrossRef Google Scholar

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