【摘要】 杂波的非高斯性会严重影响到常规信号检测算法的性能,提高非高斯杂波中信号检测性能是雷达信号检测领域的一个研究重点。本文考虑了一种在非高斯杂波中,利用先验信息进行信号检测的方法。假定杂波统计特性满足复合高斯模型,即可以表示为散斑分量与纹理分量的乘积。选择逆伽马分布作为纹理分量的先验分布,基于贝叶斯方法,给出了一种知识辅助的信号检测算法。计算机仿真结果表明,该检测算法的检测性能优于常规的自适应检测算法。进一步,本文采用McMaster大学的IPIX雷达海杂波数据作为研究对象,利用最大似然估计获得杂波非高斯性的先验信息,分析了该算法在实测数据中的检测性能。分析结果表明,在不同的雷达分辨率海杂波中,该算法也具有较好的检测性能。更多还原

【Abstract】 The conventional detection algorithms have poor signal detection performance in non-Gaussian clutter,and the signal detection in non-Gaussian clutter is an important issue in radar signal processing field.In this paper,we consider a signal detection method in non-Gaussian clutter using some prior knowledge about clutter.The statistical characteristics of clutter can be modeled as compound Gaussian process,which is the product of speckle components,which can be modeled as complex Gaussian process,and texture components.Based on the Bayesian methods,using the inverse gamma distribution as the prior distribution of texture components,a knowledge-aided signal detection algorithm is proposed.The computer simulations results show that,the knowledge-aided signal detection algorithm outperform the conventional detection algorithms. Furthermore,in this paper,we use IPIX radar sea clutter data from McMaster University as the object of study,and the prior information of non-Gaussian clutter is obtained using maximum likelihood estimation of clutter data.The detection performance of the algorithm is analyzed,and the results show that,the detection performance of this algorithm is outperform the other conventional signal detection methods,and can achieve a better detection performance in sea clutter data with different resolution.更多还原