Bearing Locating Algorithm of Target based on Radial Basis Function Neural Network
Aiming at the high locating error and the underutilization of redundancy bearing measurement in Pyroelectric Infrared Sensor (PIR) location system,a novel method of bearing location based on Radial Basis Function Neural Network (RBFNN) is presented.After illustrating the region partition model of PIR sensor node,we take advantage of the K-means clustering method and the gradient-descent method to train the neural network.By comparing different sizes of training samples,we select a neural network model with lower locating error,and we have made a comparison of RBFNN and the geometric algorithm.The result of simulation shows that the neural network model has 18% higher locating accuracy and the locating error is much less than the geometric algorithm when the target is near the boundary of the detecting area.
radial basis function neural network pyroelectric infrared sensor bearing-location
Wang Zihao Tian Jie
Key Laboratory of Military Communication,Engineering University of CAPF,Xian,China
国际会议
重庆
英文
92-97
2016-05-21（万方平台首次上网日期，不代表论文的发表时间）