一种基于粒子滤波的跳频信号频率跟踪技术

Frequency tracking technology for frequency-hopping signals using particle fiters

为了能在未知跳频模式或跳频时刻的条件下,实时估计跳频信号的频率,提出了1种相位粒子滤波的跳频信号频率跟踪算法.首先建立了跳频信号的相位统计模型;然后通过粒子滤波算法实现对信号相位的后验概率密度估计;最后通过相位的后验概率密度实现对跳频频率的最小均方误差估计.算法使用序贯重要性采样技术实现粒子权值的迭代更新,采用系统重采样技术防止粒子权重的衰退.仿真实验表明:该算法具有稳健的实时频率跟踪能力,而且当信噪比较高时,相对于重分配平滑伪Wigner-Ville分布的时频分析算法或辅助谱图粒子滤波估计算法,具有更小的估计误差.

To estimate the frequency of frequency-hopping signals in real-time without the knowledge of its hopping pattern or transition time, a phase particle filter algorithm was put forward for frequen- cy tracking. The stochastic model of frequency-hopping signalsr phase was proposed. The estimation of posterior probability density of signal phase was realized with particle filter algorithm. Finally the minimum mean square error （MMSE） estimation of hopping frequency was obtained with the posterior probability density of signal phase. The particle weights were recursively computed using sequential importance sampling （SIS）. Systematic resampling was employed for the reduction Of particle weights degeneration. Simulation results demonstrate the robust frequency tracking ability of the proposed method. In addition, compared with the reassigned smoothed pseudo Wigner-Ville distribution （RSPWVD） algorithm or the auxiliary spectrogram particle filter （ASPECT-PF） algorithm, the pro- posed method has a lower estimation error with high signal-to-noise ratio.