一种基于模糊遗传算法的多传感器多目标跟踪数据关联算法

摘要

A novel data association algorithm is developed based on fuzzy genetic algorithms (FGAs) . The static part of data association uses one FGA to determine both the lists of composite measurements and the solutions of m-best S-D assignment. In the dynamic part of data association, the results of the m-best S-D assignment are then used in turn, with a Kalman filter state estimator, in a multi-population FGA-based dynamic %2D% assignment algorithm to estimate the states of the moving targets over time. Such an assignment-based data association algorithm is demonstrated on a simulated passive sensor track formation and maintenance problem. The simulation results show its feasibility in multi-sensor multi-target tracking. Moreover, algorithm development and real-time problems are briefly discussed.%基于模糊遗传算法发展了一种新的数据关联算法.数据关联的静态部分靠一个模糊遗传算法来得出量测组合序列和S-D分配的%m%个最优解.在数据关联的动态部分,将得到的S-D分配的m个最优解在一个基于多种群模糊遗传算法的动态%2D%分配算法中依靠一个卡尔曼滤波估计器估计出移动目标各个时刻的状态.这一基于分配的数据关联算法的仿真试验内容为被动式传感器的航迹形成和维持的问题.仿真试验的结果表明该算法在多传感器多目标跟踪中应用的可行性.另外,对算法发展和实时性问题进行了简单讨论.