基于成像声呐的水下多目标跟踪研究

针对水下多目标跟踪问题，提出基于成像声呐的高效目标跟踪算法. 基于声呐的成像特点，针对声学图像中的每个像素点，建立基于信号强度的回波信号模型，提取图像中的个体目标. 采用基于序贯蒙特卡罗的概率密度假设(SMCPHD)滤波对各目标状态进行滤波，结合Auction航迹识别算法将滤波后的目标状态与已确定的航迹进行关联，实现多目标跟踪. 通过算法的仿真分析发现，该方法相对于基于数据关联型的多目标跟踪算法如联合概率数据关联(JPDA)算法、多假设跟踪(MHT)算法，大大提高了计算效率. 对采集的现场数据进行目标提取与跟踪，获得目标的跟踪轨迹.

Underwater multi-target tracking using imaging sonar

An efficient target tracking algorithm based on an imaging sonar was proposed to solve the problem of underwater multi-target tracking. The echo signal model based on signal intensity was established for each pixel point in the acoustic image according to the imaging features of the sonar in order to extract the individual target from the images. The sequential Monte Carlo probability hypothesis density (SMCPHD) filtering was applied to the target states. The Auction track recognition algorithm was used to associate the filtered target states with the identified tracks, so that the multi-target tracking was realized. The simulation analysis of the algorithm showed that the proposed method was more efficient than the multi-target tracking algorithms based on data correlation, eg. joint probabilistic data association (JPDA) and multiple hypothesis tracking (MHT). A field experiment was conducted to collect the sonar data. The tracking trajectories of all the targets were obtained after the target extraction and tracking.