在低信扰比条件下的纯方位被动跟踪中, 针对量测似然度评估粒子权重的方式对于滤波结果的不利影响, 提出了一种基于粒子残差一致性度量的粒子滤波算法.首先, 利用粒子残差实现采样粒子由状态空间到量测空间的映射变换；在此基础上, 通过置信度距离和置信度矩阵的构建及求解, 完成对于粒子权重的合理度量.新的粒子权重评估方法实现了对于最新量测信息及粒子间蕴含冗余和互补信息的充分提取和利用, 使得粒子权重度量结果更加稳定和可靠.最后, 仿真实验验证了算法的有效性.

Aiming at the disadvantageous influence of evaluating particles weights on the filtering results in the bearings-only passive tracking of low signal-to-interference ratio, a novel particle filter algorithm based on particle residual consistency measure was proposed. Firstly, particle residual was used to realize the mapping transformation of sampling particle from state space to measurement space. Then, confidence level distance and confidence level matrix were constructed to complete the reasonable evaluation of particles weights. The new method effectively extracts and uses the latest measurement information and redundancy and complementary information among particles, which makes the evaluation results of particles weights more stable and reliable. Finally, experiments demonstrate the efficiency of the proposed algorithm.