摘要
在样点随机缺失条件下研究跳频信号参数估计问题具有现实意义。针对样点缺失条件下线性时频分析方法失效的问题,提出了一种基于正交匹配追踪(orthogonal matching pursuit,OMP)和卡尔曼滤波(Kalman filter,KF)的跳频信号参数实时估计方法。该方法对信号加滑动窗,将窗内样点随机缺失建模为一个信号稀疏表示问题,傅里叶正交基作为过完备字典,利用OMP直接估计窗内信号频率而不需要恢复信号。KF针对估计得到的信号频率进行平滑,当频率跳变时,KF的频率预测值将严重偏离历史值和频率估计值,偏离程度作为跳变时刻估计的依据。仿真结果和对比实验表明,在样点没有缺失时,该方法具有更优的跳频信号参数估计性能,在滑动窗长足够并满足信号稀疏度要求时,即使在样点缺失的条件下,依然可以获得有效的跳频信号参数估计结果。
It is of practical significance to study the parameter estimation of the frequency hopping(FH)signals in the case of random missing observations.In view of the failure of the linear time frequency analysis in the case of missing observations,a frequency hopping signal parameter estimation method is proposed in this paper,based on orthogonal matching pursuit(OMP)and Kalman filter(KF).By this method,a sliding window is added to the signals,and the random missing observations in the window is modeled as a signal sparse representation.As an overcomplete dictionary,the Fourier orthonormal bases makes use of OMP to estimate the frequency of the signal in the window without restoring the signals.KF performs smoothly on the estimated signal frequency.When the frequency changes,the frequency prediction value of KF will seriously deviate from the historical value and the frequency estimated value,and the degree of deviation will offer the support for hopping time estimation.The simulation results and comparative experiments show that the parameter estimation of the proposed method outperforms that of other methods,when the observations are not missing.With the sufficient sliding window length and the eligible signal sparsity requirement,effective FH signal parameter estimation results can also be obtained even in the case of missing observations.
作者
王洪斌
张邦宁
王桁
吴彬彬
郭道省
WANG Hongbin;ZHANG Bangning;WANG Heng;WU Binbin;GUO Daoxing(College of Communications Engineering,Army Engineering University of PLA,Nanjing 210007,China)
出处
《陆军工程大学学报》
2023年第2期60-67,共8页
Journal of Army Engineering University of PLA
关键词
跳频信号参数估计
样点缺失
正交匹配追踪
卡尔曼滤波
parameter estimation of frequency hopping signals
missing observations
orthogonal matching pursuit
Kalman filter
