Title: Application of improved adaptive filtering algorithm in robust positioning of mobile robots

Authors: Zicheng Dou; Chengming Luo; Yongshuai Fei; Zizhuo Liu; Hongxuan Fan

Addresses: Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Tech, Changzhou 213000, China; Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China ' Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Tech, Changzhou 213000, China; Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China ' Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Tech, Changzhou 213000, China; Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China ' Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Tech, Changzhou 213000, China; Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China ' Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Tech, Changzhou 213000, China; Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China

Abstract: To address the low accuracy and poor reliability of adaptive filtering algorithms in mobile robot positioning systems, we propose an improved algorithm called strong tracking adaptive filtering (STAKF). Firstly, to mitigate error accumulation in strapdown inertial navigation systems (SINS), we devise a combined positioning model that integrates SINS with wireless sensor network (WSN). Secondly, recognising the challenges of noise estimation using Kalman filter in complex environments, we design an adaptive algorithm that employs exponentially fading memory weighted averages for noise estimation, ensuring appropriate adaptive capacity. Finally, by incorporating the strong tracking filter and introducing fading factor to adjust the prediction mean square error matrix online, our improved algorithm effectively handles uncertainties. Simulations demonstrate that STAKF offers superior adaptability, reduces divergence amid model errors, and maintains excellent filtering performance while enhancing stability and positioning accuracy in integrated navigation systems.

Keywords: strapdown inertial navigation systems; SINS; integrated navigation; fading factor; Sage-Husa adaptive filter; strong tracking filtering; indoor localisation; wireless sensor network; WSN.

DOI: 10.1504/IJSCC.2024.143860

International Journal of Systems, Control and Communications, 2024 Vol.15 No.4, pp.411 - 429

Received: 23 Aug 2024
Accepted: 07 Nov 2024
Published online: 10 Jan 2025 *

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