High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network

• Published in: Frontiers of information technology & electronic engineering Vol. 24; no. 8; pp. 1214 - 1230
• Main Authors: Xu, Baoxiong, Yi, Jianxin, Cheng, Feng, Gong, Ziping, Wan, Xianrong
• Format: Journal Article
• Language: English
• Published: Hangzhou Zhejiang University Press 01.08.2023; Springer Nature B.V; Electronic Information School,Wuhan University,Wuhan 430072,China
• Subjects: Accuracy; Algorithms; Approximation; Artificial neural networks; Communications Engineering; Computer Hardware; Computer Science; Computer Systems Organization and Communication Networks; Electrical Engineering; Electronics and Microelectronics; Extended Kalman filter; Instrumentation; Kalman filters; Networks; Neural networks; Radar equipment; Radar tracking; Recurrent neural networks; Regression models; Research Article; Tracking; Tracking errors; Tracking problem; Unmanned aerial vehicles; Passive radar; 跟踪精度; TN95; Target tracking; 外辐射源雷达; 深度前馈神经网络; Tracking accuracy; 滤波层; 目标跟踪; Deep feedforward neural network; Filter layer
• ISSN: 2095-9184; 2095-9230
• DOI: 10.1631/FITEE.2200260

In radar systems, target tracking errors are mainly from motion models and nonlinear measurements. When we evaluate a tracking algorithm, its tracking accuracy is the main criterion. To improve the tracking accuracy, in this paper we formulate the tracking problem into a regression model from measurements to target states. A tracking algorithm based on a modified deep feedforward neural network (MDFNN) is then proposed. In MDFNN, a filter layer is introduced to describe the temporal sequence relationship of the input measurement sequence, and the optimal measurement sequence size is analyzed. Simulations and field experimental data of the passive radar show that the accuracy of the proposed algorithm is better than those of extended Kalman filter (EKF), unscented Kalman filter (UKF), and recurrent neural network (RNN) based tracking methods under the considered scenarios.