In multi-LFM signal condition, Radon-Ambiguity Transform (RAT) of the strong LFM component has strong suppression effect on that of the weak LFM component. A method named as Recursive Filtering RAT (RFRAT) algorithm i...In multi-LFM signal condition, Radon-Ambiguity Transform (RAT) of the strong LFM component has strong suppression effect on that of the weak LFM component. A method named as Recursive Filtering RAT (RFRAT) algorithm is proposed for solving this problem. By fully using of the Maximum Likelihood (ML) estimation value of the frequency modulation rate got by RAT, RFRAT can detect the noisy multi-LFM signals out step by step. The merit of this new method is validated by an illustrative example in low Signal-to-Noise-Ratio (SNR) condition.展开更多
针对多分量线性调频信号存在重叠、漏检现象,导致波达方向的估计存在较大误差问题,提出多分量线性调频信号分数阶傅里叶域量纲归一化方法。首先给出了多分量LFM信号的阵列模型,讨论了阵列模型信号时频坐标系进行量纲归一化。通过对相干...针对多分量线性调频信号存在重叠、漏检现象,导致波达方向的估计存在较大误差问题,提出多分量线性调频信号分数阶傅里叶域量纲归一化方法。首先给出了多分量LFM信号的阵列模型,讨论了阵列模型信号时频坐标系进行量纲归一化。通过对相干环境下分数阶傅里叶域进行量纲归一化操作,提高多分量信号在分数阶傅里叶域的分辨能力,保证信源数目估计的准确性。仿真结果表明该方法正确的识别信源组数,提高多分量信号波达方向估计精确度,信噪比低于-4 d B的信号抗干扰能力明显增强。展开更多
针对多分量线性调频(linear frequency modulation,LFM)雷达信号检测和参数估计精度低、计算速度慢等问题,提出了一种基于小波变换的切割聚类拟合参数估计的算法。该方法首先通过小波变换得到信号的三维时频分布图,其次采用等高线截取...针对多分量线性调频(linear frequency modulation,LFM)雷达信号检测和参数估计精度低、计算速度慢等问题,提出了一种基于小波变换的切割聚类拟合参数估计的算法。该方法首先通过小波变换得到信号的三维时频分布图,其次采用等高线截取提取出小波脊线,再找出脊线的交点,以交点为界对小波脊线图进行切割,利用模糊C均值聚类完成各LFM分量脊线的聚类,最后分别对每段脊线进行拟合加权,从而估计出多分量LFM信号参数。仿真结果表明,与基于Hough变换检测直线方法相比,不仅在计算复杂度以及参数估计的准确度上都有较大的提升,而且当LFM信号分量达到4个以上亦有较准确的检测精度。展开更多
基金Supported by the National 973 Program(No.973-1-12)
文摘In multi-LFM signal condition, Radon-Ambiguity Transform (RAT) of the strong LFM component has strong suppression effect on that of the weak LFM component. A method named as Recursive Filtering RAT (RFRAT) algorithm is proposed for solving this problem. By fully using of the Maximum Likelihood (ML) estimation value of the frequency modulation rate got by RAT, RFRAT can detect the noisy multi-LFM signals out step by step. The merit of this new method is validated by an illustrative example in low Signal-to-Noise-Ratio (SNR) condition.
文摘针对多分量线性调频信号存在重叠、漏检现象,导致波达方向的估计存在较大误差问题,提出多分量线性调频信号分数阶傅里叶域量纲归一化方法。首先给出了多分量LFM信号的阵列模型,讨论了阵列模型信号时频坐标系进行量纲归一化。通过对相干环境下分数阶傅里叶域进行量纲归一化操作,提高多分量信号在分数阶傅里叶域的分辨能力,保证信源数目估计的准确性。仿真结果表明该方法正确的识别信源组数,提高多分量信号波达方向估计精确度,信噪比低于-4 d B的信号抗干扰能力明显增强。
文摘针对多分量线性调频(linear frequency modulation,LFM)雷达信号检测和参数估计精度低、计算速度慢等问题,提出了一种基于小波变换的切割聚类拟合参数估计的算法。该方法首先通过小波变换得到信号的三维时频分布图,其次采用等高线截取提取出小波脊线,再找出脊线的交点,以交点为界对小波脊线图进行切割,利用模糊C均值聚类完成各LFM分量脊线的聚类,最后分别对每段脊线进行拟合加权,从而估计出多分量LFM信号参数。仿真结果表明,与基于Hough变换检测直线方法相比,不仅在计算复杂度以及参数估计的准确度上都有较大的提升,而且当LFM信号分量达到4个以上亦有较准确的检测精度。
