针对前视声纳的成像特点，研究了目标多特征提取、多特征与粒子群优化粒子滤波(particle swarm optimized particle filter, PSOPF)的融合方法，设计了以自适应加权特征值为适应度值的优化跟踪算法. 该算法通过不断更新粒子群在搜索空间中的速度和位置，可实现粒子向高似然概率区域运动. 对声纳图像序列进行水下
目标跟踪实验，结果表明多特征融合PSOPF 算法可有效控制粒子贫乏和发散，提高系统鲁棒性，在降低粒子数目的同时提高了跟踪精度，满足水下目标跟踪的要求.

Particle swarm optimized particle filter (PSOPF) can deal with leanness and divergence of the traditional particle filter to some extent. However, robustness and tracking precision need to be improved as the system observation model is limited to the use of observed data. Based on the imaging mechanism of the
forward-looking sonar, extraction of multiple features and fusion of the features with PSOPF are discussed.An optimized algorithm using adaptive weighted eigenvalues as fitness is proposed. It makes particles move to the high likelihood zone by updating their speed and position. Experiments are performed to track underwater targets from sonar image sequences. The results show that the multi-feature fused PSOPF can improve robustness and decrease risk of leanness and divergence. It can achieve high tracking precision with fewer particles. The advantages make it suitable for underwater target tracking.