CAJ | 학술논문

为了减小非视距（Non Line of Sight，NLOS）误差对移动台定位精度的影响，提出一种基于遗传优化后向传播（Back Propagation，BP）神经网络的无线定位算法。先利用遗传算法优化BP神经网络的初始权值，再利用优化后的GA-BP神经网络修正到达时间差（Time Difference Of Arrival，TDOA）测量值，最后使用Chan氏算法确定移动台的位置，以避免由于神经网络初始权值的随机性所带来的网络震荡，克服网络容易陷入局部解的问题。仿真结果表明，新算法能够实现移动台的静态定位，并且性能优于传统BP 神经网络与最小二乘（Least Square，LS）算法。
위료감소비시거（Non Line of Sight，NLOS）오차대이동태정위정도적영향，제출일충기우유전우화후향전파（Back Propagation，BP）신경망락적무선정위산법。선이용유전산법우화BP신경망락적초시권치，재이용우화후적GA-BP신경망락수정도체시간차（Time Difference Of Arrival，TDOA）측량치，최후사용Chan씨산법학정이동태적위치，이피면유우신경망락초시권치적수궤성소대래적망락진탕，극복망락용역함입국부해적문제。방진결과표명，신산법능구실현이동태적정태정위，병차성능우우전통BP 신경망락여최소이승（Least Square，LS）산법。
In order to reduce the influence of NLOS errors on mobile positioning accuracy,a loca-tion algorithm based on BP neural network optimized by genetic algorithm is proposed.The ge-netic algorithm is able to correct the initial weights of BP neural network and then the NLOS er-rors can be corrected by the optimized GA-BP neural network.Furthermore,the positions of MS can be estimated by Chan ’s algorithm.This new algorithm overcomes the networks shock brought by the randomness of the initial weights and solves the problem of network solutions easy to fall into local issues.Simulation results show that the new algorithm can achieve static positio-ning of the mobile station and the performance is better than traditional BP neural network and LS algorithm.