基于改进粒子滤波算法的水下目标跟踪 

针对复杂水下环境中声探测传感器获得的运动目标信息具有不确定性和模糊性等问题，提出了基于声探测传感器特点的高斯粒子滤波水下目标跟踪方法.基于粒子滤波理论，采用一阶自回归模型作为运动目标状态转移的依据，设计了由目标区域的面积特征和不变矩特征相融合的观测模型，解决了目标跟踪中的粒子权值的选取问题，克服了传统粒子滤波重采样问题，提高了复杂环境下目标跟踪结果的准确率.展示了应用高斯粒子滤波实现水下目标跟踪的过程.试验结果表明，该方法具有较好的鲁棒性和实时性，是复杂水下环境中目标跟踪的一种高效可行的新方法.

Underwater Object Tracking Based on Improved Particle Filter

A novel method based on Gaussian particle filter (GPF) for underwater target tracking was presented aiming at the uncertain and fuzzy information of moving objects obtained by sonar sensor in complex underwater environment, which takes account of the inherent characters of sonar sensor. A first-order autoregressive-process equation was used as the support of state transition of moving object according to the particle filter theory. A measurement model combining the object region with its moment invariants was designed. The problem of particles weight selection was solved and the resample of traditional particle filter was avoided. The correct rate of object tracking under complex background was improved. The complete procedure of underwater object tracking based on Gaussian particle filter was displayed. The results show that the advanced method has satisfactory robustness and real time property. It is a feasible and -effective-way for object tracking in complex underwater environment．