Distributed path optimisation of mobile sensor networks for AOA target localisation

• Published in: IET control theory & applications Vol. 13; no. 17; pp. 2817 - 2827
• Main Authors: Yang, Ziwen, Zhu, Shanying, Chen, Cailian, Guan, Xinping, Feng, Gang
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 26.11.2019
• Subjects: AOA target localisation; arrival‐of‐angle target localisation; corresponding optimisation problem; direction‐of‐arrival estimation; distributed path optimisation; equipping sensors; estimation error; gradient methods; information filter; information‐driven mobility; localisation performance; mobile radio; mobile sensor networks; optimisation; path optimisation problem; sensor placement; Special Issue: Distributed Optimisation and Learning for Networked Systems; static sensor networks; stationary target; wireless sensor networks; mobile radio; wireless sensor networks; information filter; arrival-of-angle target localisation; mobile sensor networks; stationary target; direction-of-arrival estimation; path optimisation problem; optimisation; corresponding optimisation problem; static sensor networks; AOA target localisation; sensor placement; localisation performance; gradient methods; equipping sensors; information-driven mobility; estimation error; distributed path optimisation
• ISSN: 1751-8644; 1751-8652; 1751-8652
• DOI: 10.1049/iet-cta.2018.6112

The path optimisation problem of mobile sensor networks for arrival-of-angle (AOA) target localisation, using the consensus-based extended information filter is considered, in this study. A new idea of equipping sensors with information-driven mobility to improve the estimation accuracy with respect to a stationary target is proposed by the authors. A gradient descent method is used for mobile sensors, which are subject to geometric constraints, to choose the next optimal waypoints. The corresponding optimisation problem is solved in a distributed manner, by selecting a proper cost function for each mobile sensor. It is shown that the boundedness of the estimation error is guaranteed. Moreover, they find that the mobility of sensors does decrease the estimation error bounds compared with the static sensor networks, which is beneficial for the localisation performance. Simulation is carried out to show the effectiveness of the proposed method.