Accurate Localization Method Combining Optimized Hybrid Neural Networks for Geomagnetic Localization with Multi-Feature Dead Reckoning
Year: 2025
Type: article
Abstract: Location-based services provide significant economic and social benefits. The ubiquity, low cost, and accessibility of geomagnetism are highly advantageous for localization. However, the existing geomagnetic localization methods suffer from location ambiguity. To address these issues, we propose a fusion localization algorithm based on particle swarm optimization. First, we construct a five-dimensional hybrid LSTM (5DHLSTM) neural network model, and the 5DHLSTM network structure parameters are optimized via particle swarm optimization (PSO) to achieve geomagnetic localization. The eight-dimensional BiLSTM (8DBiLSTM) algorithm is subsequently proposed for heading estimation in dead reckoning, which effectively improves the heading accuracy. Finally, fusion localization is achieved by combining geomagnetic localization with an improved pedestrian dead reckoning (IPDR) based on an extended Kalman filter (EKF). To validate the localization performance of the proposed PSO-5DHLSTM-IPDR method, several extended experiments using Xiaomi 10 and Hi Nova 9 are conducted in two different scenarios. The experimental results demonstrate that the proposed method improves localization accuracy and has good robustness and flexibility.
Source: Sensors
DOI: 10.3390/s25051304
Authors: Suqing Yan, Baihui Luo, Xiyan Sun, Jianming Xiao, Yuanfa Ji, Kamarul Hawari bin Ghazali
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Funders: National Natural Science Foundation of China, Natural Science Foundation of Guangxi Province, Guangxi Provincial Key Laboratory of Precision Navigation Technology and Application, Guilin University of Technology, National Key Research and Development Program of China, Division of Graduate Education
Awards: G1034256320, G1149598618, G119080730, G1228272970, G1231596052, G1354095747, G1378907041, G1526630164, G1875269278, G1903105565, G1944239317, G2043929619, G2098802709, G2122140492, G2131418987, G214013620, G2404045718, G2518203671, G2742258773, G2813200744, G2844624540, G2855469915, G29317784, G305690765, G3284627471, G3301250684, G3326790855, G3385815989, G3387095240, G343049955, G3637263221, G3728794164, G3737454636, G3741060653, G4029857157, G4131762111, G4156257222, G4221222455, G4383869735, G4503946062, G4610987366, G4665809450, G5026718153, G5033620274, G506679140, G5200384454, G5310532066, G5380079817, G5422932461, G5493155099, G5644144205, G5752545338, G5767483451, G5842607977, G5874524782, G6008038063, 多模态融合韧性导航方法研究, G6190133008, G6372934721, G6440378253, G6447434417, G6614897493, G6769968031, G6799950821, G6926540769, G692962588, G7007774839, G7079315345, G7181079604, G7192539714, G7219056954, G7255667124, G7293037253, G740780825, G7550606180, G7875054507, G7953151081, G797646599, G799295298, G8035190543, G8121862143, G8130012166, G8356516052, G8439672922, G846194273, G8717747757, G8828769993, G8952801446
OpenAlex ID: W4407767738