Persymmetric Bayesian Detector in Compound Gaussian Clutter and Jamming
-
摘要: 该文对复合高斯杂波条件下存在干扰时的目标检测问题进行研究。针对自适应检测器需要一定数目独立同分布训练样本才能保证较好的检测性能，利用接收天线的反对称结构以及引入杂波协方差矩阵的先验信息的方法，利用两步广义似然比检验准则提出了该背景下的贝叶斯检测器。仿真结果表明，该文提出的检测器在训练样本数较少时具有较好的目标检测性能。Abstract: In this paper, target detection in the presence of jamming and compound Gaussian clutter is studied. For improving the better detection performance under a low number of Independent Identically Distributed (IID) training samples, the persymmetric structure of the receive antenna and the priori information of clutter covariance matrix are employed. Based on two step Generalized Likelihood Ratio Test (GLRT), the Bayesian detector is derived under this background. Simulation results show that the proposed detector achieves better performance when the number of training samples is small.
-
Key words:
- Bayesian detection /
- Persymmetric /
- Compound-Gaussian clutter /
- Jamming
-
[1] KELLY E J. An adaptive detection algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 1986, AES-22(2): 115–127. doi: 10.1109/TAES.1986.310745 [2] LIU Weijian, LIU Jun, HAO Chengpeng, et al. Multichannel adaptive signal detection: Basic theory and literature review[J]. Science China Information Sciences, 2022, 65(2): 121301. doi: 10.1007/s11432-020-3211-8 [3] LIU Weijian, LIU Jun, GAO Yongchan, et al. Multichannel signal detection in interference and noise when signal mismatch happens[J]. Signal Processing, 2020, 166: 107268. doi: 10.1016/j.sigpro.2019.107268 [4] GAO Yongchan, LIAO Guisheng, ZHU Shengqi, et al. A persymmetric GLRT for adaptive detection in compound-Gaussian clutter with random texture[J]. IEEE Signal Processing Letters, 2013, 20(6): 615–618. doi: 10.1109/LSP.2013.2259232 [5] HAO Chengpeng, MA Xiaochuan, SHANG Xiuqin, et al. Adaptive detection of distributed targets in partially homogeneous environment with Rao and Wald tests[J]. Signal Processing, 2012, 92(4): 926–930. doi: 10.1016/j.sigpro.2011.10.005 [6] SANGSTON K J and GERLACH K R. Coherent detection of radar targets in a non-Gaussian background[J]. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(2): 330–340. doi: 10.1109/7.272258 [7] DI BISCEGLIE M and GALDI C. Random walk based characterisation of radar backscatter from the sea surface[J]. IEE Proceedings-Radar, Sonar and Navigation, 1998, 145(4): 216–225. doi: 10.1049/ip-rsn:19982127 [8] SHUI Penglang, LIU Ming, and XU Shuwen. Shape-parameter-dependent coherent radar target detection in K-distributed clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(1): 451–465. doi: 10.1109/TAES.2015.140109 [9] WANG Zuozhen. Modified Rao test for distributed target detection in interference and noise[J]. Signal Processing, 2020, 172: 107578. doi: 10.1016/j.sigpro.2020.107578 [10] LIU Jun and LI Jian. False alarm rate of the GLRT for subspace signals in subspace interference plus Gaussian noise[J]. IEEE Transactions on Signal Processing, 2019, 67(11): 3058–3069. doi: 10.1109/TSP.2019.2912149 [11] LIU Weijian, WANG Yongliang, LIU Jun, et al. Performance analysis of adaptive detectors for point targets in subspace interference and Gaussian noise[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(1): 429–441. doi: 10.1109/taes.2017.2760718 [12] GAO Yongchan, LIAO Guisheng, LIU Weijian, et al. Bayesian generalised likelihood ratio tests for distributed target detection in interference and noise[J]. IET Radar, Sonar & Navigation, 2017, 11(5): 752–758. doi: 10.1049/iet-rsn.2016.0404 [13] GAO Yongchan, LIAO Guisheng, and LIU Weijian. High-resolution radar detection in interference and nonhomogeneous noise[J]. IEEE Signal Processing Letters, 2016, 23(10): 1359–1363. doi: 10.1109/LSP.2016.2597738 [14] REED I S, MALLETT J D, and BRENNAN L E. Rapid convergence rate in adaptive arrays[J]. IEEE Transactions on Aerospace and Electronic Systems, 1974, AES-10(6): 853–863. doi: 10.1109/TAES.1974.307893 [15] LI Na, YANG Haining, CUI Guolong, et al. Adaptive two-step Bayesian MIMO detectors in compound-Gaussian clutter[J]. Signal Processing, 2019, 161: 1–13. doi: 10.1016/j.sigpro.2019.03.008 [16] XUE Jian, XU Shuwen, and SHUI Penglang. Knowledge-based target detection in compound Gaussian clutter with inverse Gaussian texture[J]. Digital Signal Processing, 2019, 95: 102590. doi: 10.1016/j.dsp.2019.102590 [17] LIU Jun, LIU Weijian, TANG Bo, et al. Persymmetric adaptive detection in subspace interference plus Gaussian noise[J]. Signal Processing, 2020, 167: 107316. doi: 10.1016/j.sigpro.2019.107316 [18] LIU Jun, JIAN Tao, and LIU Weijian. Persymmetric detection of subspace signals based on multiple observations in the presence of subspace interference[J]. Signal Processing, 2021, 183: 107964. doi: 10.1016/j.sigpro.2021.107964 [19] GAO Yongchan, LIAO Guisheng, ZHU Shengqi, et al. Persymmetric adaptive detectors in homogeneous and partially homogeneous environments[J]. IEEE Transactions on Signal Processing, 2014, 62(2): 331–342. doi: 10.1109/TSP.2013.2288087 [20] MAO Linlin, GAO Yongchan, YAN Shefeng, et al. Persymmetric subspace detection in structured interference and non-homogeneous disturbance[J]. IEEE Signal Processing Letters, 2019, 26(6): 928–932. doi: 10.1109/LSP.2019.2913332 [21] PASCAL F, CHITOUR Y, OVARLEZ J P, et al. Covariance structure maximum-likelihood estimates in compound Gaussian noise: Existence and algorithm analysis[J]. IEEE Transactions on Signal Processing, 2008, 56(1): 34–48. doi: 10.1109/tsp.2007.901652 [22] TANG Mengjiao, RONG Yao, LI X R, et al. Invariance theory for adaptive detection in non-Gaussian clutter[J]. IEEE Transactions on Signal Processing, 2020, 68: 2045–2060. doi: 10.1109/TSP.2020.2981213 -
下载:
图(5)
计量
- 文章访问数: 387
- HTML全文浏览量: 119
- PDF下载量: 47
- 被引次数: 0
下载:
