【摘要】 为了减小NLOS传播的影响,基于几何结构的单次反射统计信道模型,该文提出一种NLOS环境下的TOA/AOA定位算法。利用RBF神经网络较快的学习特性和逼近任意非线性映射的能力,对NLOS传播的误差进行修正以减小NLOS传播的影响,再利用最小二乘(LS)算法进行定位,从而提高系统的定位精度。仿真结果表明,该算法在NLOS环境下有较高的定位精度,性能优于Chan算法,Taylor算法和LS算法。更多还原

【Abstract】 In order to mitigate the effect of NLOS propagation, based on the Geometry Based Single- Bounced (GBSB)statistical model, a TOA/AOA location algorithm based on the RBF neural network is proposed. The fast study and non-linear approach capacity of the neural network is made use of to correct the error of NLOS propagation, then the position is calculated by Least-Square (LS) algorithm to improve the location accuracy. The simulation results indicate that the location accuracy is significantly improved and the performance of this algorithm is better than that of Chan algorithm, Taylor algorithm and LS algorithm in NLOS environment.更多还原