基于自适应降调频的LFM信号参数估计新算法

为解决现有基于压缩感知理论的线性调频(liner frequency modulation,LFM)信号参数估计中冗余字典规模庞大的问题,提出了一种基于改进谱形熵和降调频的参数估计新算法。该算法首先进行LFM信号的步进降调频处理,通过检测改进谱形熵的极小值获取调频斜率的预估值。其次构建降调频观测矩阵,在压缩采样的同时完成信号的降调频处理。最后通过部分重构算法获得参数估计值。实验结果表明,该算法在SNR≤-3 d B和M≤N/8条件下相比于同类算法具备更好的估计效果,同时运算复杂度得到显著降低。

Novel parameter estimation algorithm for LFM signal based on adaptive down-chirp

A novel parameter estimation algorithm based on improved spectrum entropy and down-chirp was proposed, in order to solve the issue of large scale redundant dictionary in the parameter estimation of Liner Frequency Modulation (LFM) signal based on compressed sensing. Firstly, the LFM signal was processed by down-chirp step-by-step and then acquire the discreet value of the slope of frequency modulation through detecting the minimum of improved spectrum entropy. Secondly, a new measurement matrix was structured to complete the compressive sampling and down-chirp simultaneously. Finally, the parameter values were estimated through partial reconstruction algorithm. Simulation results show that this algorithm achieves better estimation effect than similar algorithms under the condition and , meanwhile the algorithm complexity cut down significantly.