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采用最大似然估计方法解决TDOA定位估计问题可以避免已有算法的缺点,适用性更强,但必须解决由此产生的非线性优化问题。人工免疫算法是一种模拟自然免疫系统功能、收敛性能较好的新兴智能方法,针对TDOA定位估计问题对人工免疫算法进行了改进,采用浮点数编码,避免了二进制编码所必须的编解码过程;并采用轮盘赌策略克隆亲和度较高的抗体,并控制抗体以不同变异率变异,提高了收敛速度和性能,改进了免疫算子。针对TDOA估计问题,联合使用Chan算法和人工免疫算法,可以取得较好的定位精度,提高收敛速度。仿真结果表明,在保证抗体数量的情况下,该算法性能稳定,并能以较快的速度收敛到全局最优解,相对于Chan算法精度更高。
The method of maximum likelihood estimation to solve TDOA location estimation problem can avoid the disadvantages of the existing algorithms and is more applicable. However, it is necessary to solve the nonlinear optimization problem. Artificial immune algorithm is an emerging intelligent method that simulates the function of natural immune system and has better convergence performance. The artificial immune algorithm is improved for TDOA location estimation problem, and floating point encoding is adopted to avoid the coding and decoding process necessary for binary encoding. The roulette strategy was used to clone the antibodies with higher affinity and to control the variation of antibody with different mutation rates, which improved the speed of convergence and performance and improved the immune operator. For TDOA estimation, the combination of Chan algorithm and artificial immune algorithm can achieve better positioning accuracy and speed up convergence. The simulation results show that the proposed algorithm is stable and can converge to the global optimal solution at a faster speed with the number of antibodies guaranteed, which is higher than the Chan algorithm.