A cost‐efficient joint target location and clock bias estimation technique using multiple time step measurements in mobile bistatic sonar. Issue 1 (8th September 2021)

• Record Type: Journal Article
• Authors: Zhang, Chenglin; Zhang, Qunfei; Shi, Wentao; Wang, Weidong; Xu, Jiajie; Pang, Feifei
• Is Part Of: IET radar, sonar & navigation. Volume 16:Issue 1(2022)
• Page Start: 91
• Page End: 102
• Publication Date: 2021-09-08
• Subjects: linearisation techniques -- recursive estimation -- synchronisation -- time‐ofarrival estimation; time‐of‐arrival estimation -- maximum likelihood estimation -- Gaussian noise -- mean square error methods -- optimisation -- target tracking -- sonar signal processing; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/rsn2.12166
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract: Active localization of a stationary target is one key issue in applications of bistatic sonar based on bistatic range (BR) measurement. However, the complex underwater environment makes achieving the synchronization between the transmitted station and the received station difficult. For solving the synchronization problem, this study relies on mobile bistatic sonar to acquire measurement information at multiple time steps. After that, the target location and clock bias are jointly estimated by solving a non‐linear least square problem. To this end, a recursive scheme is proposed to optimise the measurement cost and reduce the impact of the initial value to achieve accurate estimation by taking full advantage of the redundant measurement information. Orthogonal transformation is used to ensure computational efficiency and numerical reliability. The Cramer–Rao Lower Bound (CRLB) is also derived as a lower bound on the error in estimating the target position and clock bias. Simulation results show that the proposed method achieves the CRLB performance and optimises the measurement cost over the small error region under Gaussian noise.