基于WVD改进算法的跳频信号参数估计

针对平滑伪维格纳分布（SPWVD：SmooLhedPseudoWigner—VilleDistribution）在跳频信号参数估计中存在的分辨率下降和运算量过大等问题，提出基于sTfTr（Short—TimeFourierTransform）与WVD（Wigner—VilleDistribution）的联合算法（STFT—WVD），实现了跳频周期、跳变时刻和跳频频率等参数的盲估计。在参数估计的过程中，STFT—WVD算法有效地抑制了WVD的交叉干扰项，保持了WVD较高的时频分辨率，在运算量上和WVD相近，没有增加运算的复杂程度。仿真实验证明，STFT—WVD比SPWVD具有更好的整体性能，在信噪比优于-1dB时，能得到更为精准的估计值。

Parameter Estimation of Frequency-Hopping Signals Based on Amelioration Algorithm of WVD

An amelioration algorithm to the FH （Frequency-Hopping） signals parameter estimation, United Algorithm basted on STFT （ Short-Time Fourier Transform） and WVD （ Wigner-Ville Distribution）, was proposed to solve the problems of failing time-frequency resolution and oversize operation quantity, which occur in FH sig- nals parameter estimation with smoothed Pseudo WVD （SPWVD）. STFT-WVD could achieve FH signals parameters blind estimation successfully, which include hop-duration, hop-timing and hop-frequency. In parameter estimation, STFT-WVD suppresses the cross-term interference in WVD effectively, and keeps the excellent time- frequency resolution as WVD. The operation quantity is almost equivalent between STFT-WVD and WVD, the STFT-WVD does not increase the complexity degree of the operation. Finally, the simulation result proves that the holistic performance of STFT-WVD is better than SPWVD, and STFT-WVD can get more precise parameter estimated values while SNR exceeds -1 dB.