中文摘要：

目的为了克服最小二乘法在TDOA／AOA无线定位算法中的缺点，提高定位的精度和稳定性。方法提出了基于RBF神经网络的TDOA／AOA定位算法。利用染色体长度可调编码方式的模拟退火遗传算法，同时训练RBF网络的参数和拓扑结构，将训练后的RBF网络用于TDOA／AOA定位，结果与传统TDOA／AOA算法和基于后．均值聚类法、遗传算法RBF神经网络、GA-BP神经网络的定位算法比较，该算法具有更高的定位精度和可靠性。结论基于模拟退火遗传算法RBF神经网络的TDOA／AOA定位算法有很强的抗NLOS能力。

英文摘要：

Aim In order to solve the problem of least-square-method in wireless location algorithm, and improve the accuracy and stability of the location algorithm. Methods An algorithm based on the RBF neural network is proposed. The network parameters and topology structure of RBF networks are trained in the simulated annealing and genetic algorithm based on chromosome with adjustable length, Then the trained RBF networks are used to TDOA/AOA location algorithm. Results The location accuracy is significantly improved and the stability of this algorithm is better than that of TDOK/AOA algorithm and the algorithm based on the RBF neural networks trained with k-mean and genetic algorithm and GA-BP neural networks in NLOS environment. Conclusion The algorithm can mitigate the error introduced by NLOS.