Maneuvering Target Tracking Based on Neural Network and Error Self-correction Technology

Abstract

Neural network has strong nonlinear data characterization ability and solves many complex problems successfully. Trajectory estimation and prediction is time series forecasting but different from convention problems such as time video analysis. A method based on neural network and error self-correction technology achieving trajectory estimation and prediction is proposed in this paper. The method needs neural network without additional filtering algorithm, so the maneuver models and noise characteristics are not needed. According to the information of the previous moments before the investigated time, the information of the next moment or a specified time later can be obtained. For tracking a simple maneuvering target model with unknown parameters and noise characteristics, numerical simulation results show that FNN achieves filtering and it achieves a higher prediction accuracy than the Least Squares filtering. For tracking a complex maneuvering target model with strong nonlinearity, RNN combining with FNN is employed. For the measurement error with D standard deviation 2m, azimuth angle and the altitude angle measurement errors standard deviation with 2mil, the angle predicting error standard deviation is less than 1.3mil, which shows RNN combing with error self-correction technology has high accuracy. It meets the technical requirements for maneuvering target tracking as well as various similar applications.