Data association algorithm for multi-infrared-sensor system

When established association cost, traditional multi-dimension assignment data association algorithm for multi-infrared-sensor ignored the random errors introduced by least square estimation. To overcome the problem, a modified cost function that can get second-order accuracy was proposed. The first two items of Taylor series for nonlinear measurement function was kept down. The pseudo measurements can be got by using the mean and covariance of position estimation according to the preserved series. Then the statistical distances between real measurements and pseudo ones worked as the association costs. Finally, the correct data association ratio of the several association algorithms were compared through simulation experiments. Simulation results show that the modified cost function can reflect the association probability more accurately and the algorithm based on it can achieve better performance than the others.