采用代价参考粒子滤波器估计天波雷达目标状态

Target state estimation of the over-the-horizon radar using the cost-reference particle filter

针对天波雷达背景噪声强度大、统计特性未知等特点,采用代价参考粒子滤波器估计天波超视距雷达目标状态,通过分析重采样过程和样本贫乏问题,提出通过归一化样本的代价和风险,或提高样本数等方式来改善样本的贫乏问题,从而提高代价参考粒子滤波器在天波雷达目标状态估计中的性能.实验结果表明,与传统的粒子滤波器相比,代价参考粒子滤波器无需背景噪声的统计信息,要求的粒子数少,计算量小.

The cost-reference particle filter （CRPF） is applied for estimating the state sequence of the target in the over-the-horizon radar （OTHR）,where the background noise level is high and obeys unknown distribution.On the basis of analyzing the resampling process and the sample impoverishment in the CRPF,it is proposed that the sample impoverishment can be alleviated by normalizing the costs and risks,or increasing the number of particles.And the tracking performance of the CRPF in OTHR target state estimates can be improved.Simulation results illustrate that,compared with the classical particle filters,the CRPF which needs none of the statistics of the signals in the system requires a smaller number of particles and consumes a less computational resource.