A method for localization using network and analysis of data error

Abstract

Facing the problem of radiator target localization in a specific airspace, this paper puts forward the idea of time difference of arrival (TDOA) localization using the neural network. In the paper, we build a TDOA model for targets in an airspace to obtain the position and the TDOA value pairs, and then use them for a large amount of network training. The trained network can perform real-time localization. Furthermore, the paper also deduces the rationality of the error contained in the TDOA data in the training model, which proves the improvement of network performance in this case. Finally, the coefficient of determination is introduced to measure the training effect of the network. The experimental results verify the feasibility of using neural network for localization, and it can obtain better effectiveness and robustness compared with traditional algorithms. Also, a large number of error experiments are used to verify that training data needs to be added with appropriate errors based on actual localization scenarios to improve localization performance.