Yan Suqing, Luo Baihui, Sun Xiyan, Xiao Jianming, Ji Yuanfa, Ghazali Kamarul Hawari Bin
Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China.
School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel). 2025 Feb 20;25(5):1304. doi: 10.3390/s25051304.
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.
基于位置的服务带来了显著的经济和社会效益。地磁场的无处不在、低成本和易获取性对定位非常有利。然而，现有的地磁定位方法存在位置模糊性问题。为了解决这些问题，我们提出了一种基于粒子群优化的融合定位算法。首先，我们构建了一个五维混合长短期记忆（5DHLSTM）神经网络模型，并通过粒子群优化（PSO）对地磁定位进行优化，以实现5DHLSTM网络结构参数的优化。随后，提出了八维双向长短期记忆（8DBiLSTM）算法用于航位推算中的航向估计，有效提高了航向精度。最后，通过将地磁定位与基于扩展卡尔曼滤波器（EKF）的改进型行人航位推算（IPDR）相结合，实现了融合定位。为了验证所提出的PSO-5DHLSTM-IPDR方法的定位性能，在两种不同场景下使用小米10和Hi Nova 9进行了多次扩展实验。实验结果表明，所提出的方法提高了定位精度，具有良好的鲁棒性和灵活性。
