Location estimation based on feature mode matching with deep network models

Abstract

IntroductionGlobal navigation satellite system (GNSS) signals can be lost in viaducts, urban canyons, and tunnel environments. It has been a significant challenge to achieve the accurate location of pedestrians during Global Positioning System (GPS) signal outages. This paper proposes a location estimation only with inertial measurements.MethodsA method is designed based on deep network models with feature mode matching. First, a framework is designed to extract the features of inertial measurements and match them with deep networks. Second, feature extraction and classification methods are investigated to achieve mode partitioning and to lay the foundation for checking different deep networks. Third, typical deep network models are analyzed to match various features. The selected models can be trained for different modes of inertial measurements to obtain localization information. The experiments are performed with the inertial mileage dataset from Oxford University.Results and discussionThe results demonstrate that the appropriate networks based on different feature modes have more accurate position estimation, which can improve the localization accuracy of pedestrians in GPS signal outages.

Enhancing Pure Inertial Navigation Accuracy through a Redundant High-Precision Accelerometer-Based Method Utilizing Neural Networks

“…Song et al proposed NN-DR [ 15 ], which uses neural networks to explore the time-varying relationship between acceleration and pitch angle using a single accelerometer. Research by Bai et al [ 16 ] introduced a novel approach for location estimation using feature mode matching with deep network models, demonstrating that aligning specific deep learning models with categorized motion features significantly enhances the accuracy of pedestrian location estimation in environments where GPS is unavailable. Research by Li et al [ 17 ] unveiled a cutting-edge hybrid algorithm that merges a Gated Recurrent Unit (GRU) neural network with an interactive multiple model adaptive robust cubature Kalman filter (IMM-ARCKF) aimed at refining the accuracy of the INS/GPS-integrated navigation system amidst GPS disruptions.…”