基于免疫算法的TDOA定位技术研究

为了解决TDOA定位估计中遇到的非线性最优化问题，提出了一种联合使用Chan算法和免疫算法的混合定位算法．针对TDOA方式进行最佳坐标搜索的问题，所设计的基于浮点数编码的免疫算法利用混沌方程产生初始种群、改进了免疫算子，提高了算法的收敛速度和性能．仿真结果表明，在保证种群数量的情况下，该算法性能稳定，能找到逼近全局最优点的解，相对于Chan算法精度更高，相对于遗传算法在保证收敛性能的前提下有更快的收敛速度．

Study of TDOA location technology based on immune algorithm

In order to resolve the nonlinear optimization problem of TDOA Location, a hybrid method that employs a modified immune algorithm and a Chan algorithm is proposed. The modified immune algorithm is a consociation of genetic algorithm based on chaotic initial population and floating point code with modified immune operator that reduces the computational complexity by providing faster convergence. Simulation results show that if the population size is big enough, the algorithm is robust and can find the coordinates of near optimization. It has a higher accuracy than Chan algorithm and a faster convergence than genetic algorithm.