FGAs-Based Data Association Algorithm for Multi-sensor Multi-target Tracking

A novel data association algorithm is developed basal 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.