Work

Time-Frequency Analysis and Neural Network-Based Multipath Mitigation Method for Precise Positioning

Year: 2025

Type: article

Abstract: Multipath errors caused by signal reflection are challenging to correct when compared to other errors in radio navigation. Specifically, multipath error for indoor positioning is a major error because various materials that surround the environment can cause errors through reflected signals. This study focuses on mitigating multipath in single-transmitter-based indoor positioning, which employs a transmitter emitting multi-channel signals and uses carrier-phase measurements for precise positioning with low noise. Owing to the difficulty in accurately modelling reflected signals mathematically, this study proposes a neural-network-based technique to detect and categorize multipath-distorted signals using short-time Fourier transform (STFT) to extract features in the time-frequency domain. The STFT magnitudes in the spectrogram are used to train and validate the neural networks that detect and isolate multipath-distorted measurements to minimize positioning errors. This paper also presents a novel method that employs a trained neural network to classify the most contaminated signals by considering a single-transmitter-based positioning system with an antenna array. Experiments conducted under challenging indoor conditions demonstrate an approximately 30% reduction in the positioning error after applying the proposed multipath mitigation methods.

Source: IEEE Access

DOI: 10.1109/access.2025.3561290

Authors: Min-Ji Kim, O-Jong Kim, Jikang Lee, Changdon Kee, Juhyun Maeng

Institutions: Sejong University, Seoul National University, Korea Aerospace Research Institute

Language: English

Topic: GNSS positioning and interference

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funder: Ministry of Science and ICT, South Korea