基于IA-BP神经网络的UWB室内定位系统

复杂的室内环境给定位系统带来非视距误差和多径干扰，消除或降低误差成为超宽带(UWB)室内定位研究的热点。提出一种基于IA-BP神经网络的UWB室内定位方法，将BP神经网络训练的误差值作为免疫算法计算亲和度的抗原，通过免疫算法寻得BP神经网络的最优权值和阈值，避免BP神经网络收敛速度较慢和容易陷入局部最优值的问题，达到定位误差较小的目的。仿真实验结果表明，IA-BP神经网络训练100个样本输出的最大归一化误差不超过0.02，以3个锚点构成的定位场景中，待定位节点的仿真输出轨迹与实际运动轨迹基本吻合。

UWB indoor localization system based on IA-BP neural network

The complex indoor environment brings non-visual range error and multipath interference to the localization system, How to eliminate or reduce error becomes a hot spot in research to UWB indoor localization. A UWB indoor localization method based on IA-BP neural network is proposed, Which is that the error of training by BP neural network is as the antigen of calculating affinity to immune algorithm,the optimal weight and threshold of BP neural network are obtained through immune algorithm, so as to avoid the problem of slow convergence and getting into easily local optimal value of BP neural network, then get the minimum localization error. Simulation results show that the maximum error from training to 100 samples by IA-BP neural network was not more than 0.02, In the positioning scene composed of three anchors, the simulation output trajectory of the undetermined bit node was basically consistent with the actual motion trajectory.