关键词: 到达时间差定位法, 高斯变异, 克隆选择, 最大似然估计

Abstract: In order to resolve the nonlinear optimization problem in the traditional TDOA-based location, this paper firstly turns it to a problem of searching peak value by using the max likelihood estimation and then presents a new improved immune algorithm based on the clone selection theory. The new algorithm adopts a float point encoding mode to improve efficiency of the algorithm and uses the Gauss mutation which is controlled by the affinity to improve the ability of searching part of area. Numerical simulations show that this new algorithm has higher accuracy and better performance in global searching.

Key words: Time Difference of Arrival（TDOA）, Gauss mutation, clone selection, Maximum Likelihood Estimation（MLE）