Qinghua Yang,∗ Zhipeng Yuan,∗ and Changfa Wang∗
Indoor localisation, Wi-Fi fine time measurement (FTM), localisation algorithm, non-line-of-sight (NLOS) conditions ∗ School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 201900, China; e-mail: yqh [email protected]; [email protected]; wangchangfa [email protected] Corresponding author: Qinghua Yang
This paper presents an indoor positioning system developed on embedded devices, proposing a robust indoor positioning approach that integrates adaptive particle filtering and Wi-Fi fine time measurement (FTM). The system first identifies the types of errors to categorise the FTM ranging data into two categories: line-of-sight (LOS) and non-LOS (NLOS). For NLOS data, a constant-speed variable-direction error compensation model is employed for correction. Subsequently, the enhanced dragonfly optimisation algorithm is combined with particle filtering to create the adaptive particle filter algorithm (DAPF). The compensated ranging data is subsequently input into this algorithm for iterative computation, ultimately yielding accurate positioning results. This study implemented the FTM ranging and positioning module on embedded devices and performed experimental validation in a non-line-of-sight indoor environment. The results indicate that the NLOS error compensation model achieves approximately a 24% performance improvement. Utilising only four access point nodes, the error-compensated DAPF algorithm attains an average positioning accuracy of 1.87 m, with 38.4% of the measurements achieving meter-level precision. Compared to the traditional particle filter algorithm, this method enhances positioning accuracy by 47% in NLOS environments, demonstrating overall performance that is superior to other common positioning methods.
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