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摘要: 在复杂电磁环境下，往往需要在线估计杂波协方差矩阵，从而自适应调整滤波器权值，实现对杂波的有效抑制，这样有利于目标的估计、检测、定位或跟踪。该文考虑非高斯杂波模型，且部分杂波受到子空间信号干扰，并且有用信号也位于该子空间内。常规方法会导致自适应滤波器在目标多普勒频率处有较大的衰减，极大影响了有用信号的探测。为此提出了一种知识辅助的分层贝叶斯模型，采用变分贝叶斯推断方法获得杂波协方差矩阵的近似后验分布，利用后验均值设计杂波抑制滤波器，可以有效提高目标的探测性能。计算机仿真和实测数据验证结果表明，该方法能够有效抑制杂波，而在目标处有较好的探测能力。Abstract: In complex electromagnetic environments, a clutter covariance matrix is required to estimate in the on-line manner, so as to adaptively adjust the filter weight to effectively suppress clutter, thereby improving target estimation, detection, location, and tracking. In this paper, a non-Gaussian clutter model is considered, while apart of the clutter data maybe contaminated by subspace interference, wherein the signal of interest is located in the subspace. To this end, we propose a knowledge-aided hierarchical Bayesian model and obtain the approximated posterior distribution of the clutter covariance matrix by exploiting variational Bayesian inference methods. The target detection performance can be enhanced using a clutter-suppression filter that is designed based on the posterior mean of the clutter covariance matrix. A comparison of the computer simulation results with real clutter data confirms that the proposed method can suppress the clutter and improve detection performance.
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图 1 仿真数据η=17.5%时干扰识别与杂波抑制
Figure 1. Interference identification and clutter suppression with η=17.5% using simulated data
图 2 仿真数据η=40.0%时干扰识别与杂波抑制
Figure 2. Interference identification and clutter suppression with η=40.0% using simulated data
图 3 仿真数据η=77.5%时干扰识别与杂波抑制
Figure 3. Interference identification and clutter suppression with η=77.5% using simulated data
图 4 实测数据(30 m分辨率)η=40.0%时干扰识别与杂波抑制
Figure 4. Interference identification and clutter suppression with η=40.0% using IPIX dataset (30 m resolution)
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