辅助数据缺失环境下结构化自适应目标检测器
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摘要
杂波背景非均匀导致经典自适应检测方法性能下降,为改善辅助数据缺失环境下的目标检测性能,研究了雷达目标结构化自适应检测方法。利用杂波协方差矩阵斜对称特征,设计了结构化广义似然比检验自适应检测器,提高了杂波谱结构化信息和待检测单元杂波信息的利用率;进一步推导了检测器的实数域表达式。仿真分析表明,所提检测器具有恒虚警率特性,其检测性能对不同杂波相关性具有很好的鲁棒性。在辅助数据缺失环境下,所提检测器性能优于现有典型非结构化和结构化检测器,且这种优势随着辅助数据缺失程度的增大而加大。
Non-homogeneous background clutter usually results in the performance degradation of traditional adaptive detectors.To improve the detection performance in scarce secondary data environment,an adaptive structured detector of radar targets is addressed herein.By utilizing the persymmetry of the clutter covariance matrix,an adaptive structured detector is designed based on the generalized likelihood ratio test,which enhances the information utilization of the structural character of clutter spectral properties and the clutter in the cell under test.In the sequel,a real domain version is derived.The experimental results show that,the proposed detector has the constant false alarm rate and it performs robustly for different correlations of clutter.It is shown that,the proposed detector outperforms the traditional structured and unstructured detectors in scarce secondary data environment;and this advantage augments as the number of secondary data approaches the available minimum limit.
Non-homogeneous background clutter usually results in the performance degradation of traditional adaptive detectors.To improve the detection performance in scarce secondary data environment,an adaptive structured detector of radar targets is addressed herein.By utilizing the persymmetry of the clutter covariance matrix,an adaptive structured detector is designed based on the generalized likelihood ratio test,which enhances the information utilization of the structural character of clutter spectral properties and the clutter in the cell under test.In the sequel,a real domain version is derived.The experimental results show that,the proposed detector has the constant false alarm rate and it performs robustly for different correlations of clutter.It is shown that,the proposed detector outperforms the traditional structured and unstructured detectors in scarce secondary data environment;and this advantage augments as the number of secondary data approaches the available minimum limit.
引文
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[9]CONTE E,DE MAIO A.Mitigation techniques for non-Gaussian sea clutter[J].IEEE Journal of Oceanic Engineering,2004,29(2):284-302.
[10]HAO C P,ORLANDO D,FOGLIA G.Persymmetric adaptive detection of laboratory distributed targets in partially homogeneous environments[J].Digital Signal Processing,2012,131(4):42-51.
[11]DE MAIO A,FOGLIA G,CONTE E,et al.CFAR behavior of adaptive detectors:an experimental analysis[J].IEEE Trans.on Aerospace and Electronic Systems,2005,41(1):233-251.
[12]NITZBERG R.Application of maximum likelihood estimation of persymmetric covariance matrices to adaptive processing[J].IEEE Trans.on Aerospace and Electronic Systems,1980,16(1):124-127.
[13]PAILLOUX G,FORSTER P,OVARLEZ J P.Persymmetric adaptive radar detectors[J].IEEE Trans.on Aerospace and Electronic Systems,2011,47(4):2376-2390.
[14]GAO Y C,LIAO G S,ZHU S Q,et al.Persymmetric adaptive detectors in homogeneous and partially homogeneous environments[J].IEEE Trans.on Signal Processing,2014,62(2):331-342.
[15]GABRIEL J R,KAY S M.On the relationship between the GLRT and UMPI tests for the detection of signals with unknown parameters[J].IEEE Trans.on Signal Processing,2005,53(11):4194-4203.
[16]KELLY E J,FORSYTHE K M.Adaptive detection and parameter estimation for multidimensional signal models[R].Lexington:MIT Lincoln Laboratory Technology,1989.
[2]KELLY E J.An adaptive detection algorithm[J].IEEE Trans.on Aerospace and Electronic Systems,1986,22(1):115-127.
[3]ROBEY F C,FUHRMANN D R,KELLY E J,et al.A CFAR adaptive matched filter detector[J].IEEE Trans.on Aerospace and Electronic Systems,1992,28(1):208-216.
[4]简涛,何友,苏峰,等.高距离分辨率雷达目标检测研究现状与进展[J].宇航学报,2010,31(12):2623-2628.JIAN T,HE Y,SU F,et al.Overview of high range resolution radar target detection[J].Acta Aeronoutica et Astronatica Sinica,2010,31(12):2623-2628.
[5]朱秋明,徐大专,陈小敏.二维非均匀散射信道改进模型[J].系统工程与电子技术,2010,32(10):2049-2052.ZHU Q M,XU D Z,CHEN X M.Improved channel model under 2Dnon-isotropic scattering conditions[J].Systems Engineering and Electronics,2010,32(10):2049-2052.
[6]BRESLER Y.Maximum likelihood estimation of a linearly structured covariance with application to antenna array processing[C]∥Proc.of the 4th Annual ASSP Workshop on Spectral Estimation and Modeling,1988:172-175.
[7]JIAN T,HE Y,SU F,et al.Adaptive detection of range-spread targets without secondary data in multichannel autoregressive process[J].Digital Signal Processing,2013,23(5):1686-1694.
[8]邹鲲,廖桂生,李军,等.基于Bayesian框架的复合高斯杂波下稳健检测[J].电子与信息学报,2013,35(7):1551-1560.ZOU K,LIAO G S,LI J,et al.Robust detection in compound Gaussian clutter based on Bayesian framework[J].Journal of Electronics&Information Technology,2013,35(7):1551-1560.
[9]CONTE E,DE MAIO A.Mitigation techniques for non-Gaussian sea clutter[J].IEEE Journal of Oceanic Engineering,2004,29(2):284-302.
[10]HAO C P,ORLANDO D,FOGLIA G.Persymmetric adaptive detection of laboratory distributed targets in partially homogeneous environments[J].Digital Signal Processing,2012,131(4):42-51.
[11]DE MAIO A,FOGLIA G,CONTE E,et al.CFAR behavior of adaptive detectors:an experimental analysis[J].IEEE Trans.on Aerospace and Electronic Systems,2005,41(1):233-251.
[12]NITZBERG R.Application of maximum likelihood estimation of persymmetric covariance matrices to adaptive processing[J].IEEE Trans.on Aerospace and Electronic Systems,1980,16(1):124-127.
[13]PAILLOUX G,FORSTER P,OVARLEZ J P.Persymmetric adaptive radar detectors[J].IEEE Trans.on Aerospace and Electronic Systems,2011,47(4):2376-2390.
[14]GAO Y C,LIAO G S,ZHU S Q,et al.Persymmetric adaptive detectors in homogeneous and partially homogeneous environments[J].IEEE Trans.on Signal Processing,2014,62(2):331-342.
[15]GABRIEL J R,KAY S M.On the relationship between the GLRT and UMPI tests for the detection of signals with unknown parameters[J].IEEE Trans.on Signal Processing,2005,53(11):4194-4203.
[16]KELLY E J,FORSYTHE K M.Adaptive detection and parameter estimation for multidimensional signal models[R].Lexington:MIT Lincoln Laboratory Technology,1989.