Target Localization and Clock Refinement in Distributed MIMO Radar Systems With Time Synchronization Errors

• Published in: IEEE transactions on signal processing Vol. 69; pp. 3088 - 3103
• Main Authors: Song, Haibo, Wen, Gongjian, Liang, Yuanyuan, Zhu, Lingxiao, Luo, Dengsanlang
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation algorithms; Clock synchronization; Clocks; Computer simulation; Cramer-Rao bounds; Error analysis; Exact solutions; Localization; Localization method; Location awareness; Lower bounds; Measurement uncertainty; MIMO (control systems); MIMO radar; multiple-input multiple-output radar; Radar equipment; Radar systems; Sensor systems; Signal processing algorithms; Simulation; Target localization; time delay; Time lag; Time measurement; Time synchronization
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2021.3081038

Achieving accurate clock synchronization may not be feasible for distributed multiple-input multiple-output (MIMO) radar systems with non-ideal clocks of the sensors. In this paper, the problem of target localization and clock synchronization in distributed MIMO radar systems in presence of time synchronization errors is addressed by utilizing time delay measurements. Firstly, the necessity to consider time synchronization errors when locating a target is illustrated by analyzing the increase in Cramer-Rao lower bound (CRLB) of the target position due to the time synchronization error. Then, a self-synchronized localization framework based on the hybrid maximum likelihood and maximum a posteriori estimator is proposed to estimate the target position and time synchronization error sequentially. Specifically, a method based on the levenberg marquardt algorithm and an analytical solution are developed respectively to estimate the target position, and a combination strategy is recommended further based on the two methods to achieve a more accurate target position estimate. The time synchronization error is figured out then by utilizing the target position estimate. Meanwhile, the closed-form solution of estimating the target position and time synchronization error is analytically proved to be approximately unbiased and can attain the CRLB under weak noise conditions. It is demonstrated through numerical simulations that the combination strategy has better estimation performance than the two methods. Simultaneously, simulation results show that the proposed self-synchronized localization method outperforms the state-of-the-art algorithms in the target position and time synchronization error estimation accuracy.