Parametric multichannel target detection in heterogeneous environment

被引：0
作者：
Shang X.-Q. [1 ,2 ]
Song H.-J. [1 ]
Xu H.-S. [1 ,2 ]
Zheng J.-B. [1 ,2 ]
机构：
[1] Institute of Electronics, Chinese Academy of Sciences
[2] Graduate University of the Chinese Academy of Sciences
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2011年 / 33卷 / 05期
关键词：
Heterogeneous environments; Inverse Wishart distribution; Parametric Adaptive Matched Filter (PAMF); Target detection; Vector AutoregRessive (VAR) model;
D O I：
10.3724/SP.J.1146.2010.00949
中图分类号：
学科分类号：
摘要：
Parametric multichannel target detection in heterogeneous environment is studied in this paper, where the disturbances are represented by Vector AutoregRessive (VAR) model with its spatial covariance matrix following complex inverse Wishart distribution with degrees of freedom μ and mean Q̄. When Q̄ is unknown, the Neyman-Pearson Parametric Adaptive Matched Filter (NP-PAMF), Bayesian PAMF (B-PAMF) and its normalized version (B-NPAMF) are proposed based on NP detection rule. And when it is known, the maximum a-posteriori PAMF (MAP-PAMF) and its normalized version (MAP-NPAMF) are proposed followed MAP decision rule. It is shown that NP-PAMF and B-PAMF are both dependent on μ and B-PAMF is convergent to the PAMF when μ → ∞; B-NPAMF has no relation with μ and is consistent with the classic NPAMF. In MAP-PAMF, the MAP estimator of the spatial covariance matrix consists of the classic estimator and the prior knowledge, and the weigh of the later is controlled by μ. Finally, the complex issues and the detection performances are analyzed, showing that: Bayesian parametric detectors possess good performances and they are better than their normalized counterparts.
引用
收藏
页码：1095 / 1100
页数：5
相关论文
共 13 条
  • [1] Sangston K.J., Gini F., Greco M.V., Et al., Structures for radar detection in compound Gaussian clutter, IEEE Transactions on Aerospace and Electronic Systems, 35, 2, pp. 445-458, (1999)
  • [2] Shuai X., Kong L., Yang J., Performance analysis of GLRT-based adaptive detector for distributed targets in compound-Gaussian clutter, Signal Processing, 90, 1, pp. 16-23, (2010)
  • [3] Roy L.P., Kumar R.V.R., Performance deterioration of the matched filter detector in partially correlated texture based compound-Gaussian clutter environment, IEEE Radar Conference, pp. 1-5, (2009)
  • [4] Wang J., Dogandzic A., Nehorai A., Maximum likelihood estimation of compound-Gaussian clutter and target parameters, IEEE Transactions on Signal Processing, 54, 10, pp. 3884-3898, (2006)
  • [5] Mcwhorter L.T., Scharf L.L., Adaptive matched subspace detectors and adaptive coherence estimators, Proceedings of 30th Asilomar Conference on Signals, Systems, and Computers, pp. 1114-1117, (1996)
  • [6] De Maio A., Farina A., Foglia G., Adaptive radar detection: A Bayesian approach, Proceedings of the 2007 IEEE Int. Conf. Radar, pp. 624-629, (2007)
  • [7] Bidon S., Besson O., Tourneret J.-Y., The adaptive coherence estimator is the generalized likelihood ratio test for a class of heterogeneous environments, IEEE Signal Processing Letters, 15, pp. 281-284, (2008)
  • [8] De Maio A., Nicola S.D., Huang Y., Et al., Adaptive detection and estimation in the presence of useful signal and interference mismatches, IEEE Transactions on Signal Processing, 57, 2, pp. 436-450, (2009)
  • [9] Michels J.H., Multichannel detection using the discrete-time model-based innovations approach, (1991)
  • [10] Roman J.R., Rangaswamy M., Davis D.W., Et al., Parametric adaptive matched filter for airborne radar applications, IEEE Transactions on Aerospace and Electronic Systems, 36, 2, pp. 677-692, (2000)
12