Research on indoor single-station precise passive positioning technology based on neural network

In order to improve the positioning accuracy of targets in the indoor environment, a single-station precise passive positioning technology is proposed in this paper. Based on the complex channel information formed by the multipath effect of indoor radio wave propagation, a convolutional neural network (CNN) architecture is constructed, which can extract the multipath delay characteristic information from different location targets to the receiving sensor. Then, a deep network with several dense layers is designed to train the convoluted data, in which way, we transform the location problem based on complex channels into the problem of regression model and establish the nonlinear relationship between signal fingerprint and position, and finally the passive location of the target can be realized. The training and simulation results show that in the indoor environment of complex radio wave propagation, the method we proposed can accurately locate the target in the case of a single receiving station. In this paper, we mainly locate the metal scatterer with the size of 3×3 grids. When the receiving sensor is located indoors, the average positioning error is 0.621 grid(12.42 cm). When outdoors, the average positioning error is 44.09 cm, 21.42 cm, 20.96 cm under the SNR of 20, 30, 40 dB, respectively, which provides a new method for indoor target positioning of complex environments.