Abstract
Smartphone-based indoor navigation systems are becoming increasingly popular in a variety of applications. However, localization accuracy has always been a challenge. The Kalman filter (KF) is a well-known estimation in the Bayesian framework, but can only deal with linear problems and Gaussian models. A particle filter (PF) is another essential estimation tool in a Bayesian system. However, a critical challenge with PF is the problem of particle degradation after resampling. To mitigate the particle degradation problem in PF, unsupervised learning based on k-means clustering is proposed in this paper. It forms clusters of similar particles based on the sum of weights. Also, we present enhancing the PF by utilizing a map constraint and k-means clustering (PFMK) and integrating Bluetooth low energy (BLE) along with pedestrian dead reckoning (PDR) for positioning. BLE and PDR-based positioning with a map constraint lead to an increase in accuracy of at least 20% compared with a traditional PF. Moreover, the proposed unsupervised k-means approach increases the accuracy by an additional 20%, whereas the overall performance of PFMK achieves a mean error of <1.5 m in the test environments.
Highlights
The proliferation of smartphones and the Internet of Things (IoT) has contributed to a broad range of services
We proposed a method for improving the accuracy of navigation by utilizing map constraints and unsupervised k-means clustering
Utilizing unsupervised k-means clustering in addition to map constraints, we propose an improved particle filter (PF) algorithm that selects the best cluster of particles for location estimation
Summary
The proliferation of smartphones and the Internet of Things (IoT) has contributed to a broad range of services (e.g., localization services). A map restriction is proposed and combined with a k-means clustering-based unsupervised learning algorithm (PFMK) to avoid navigation drift and address particle degradation. The extended Kalman filter (EKF) and unscented Kalman filter (UKF) are two KF alternatives that can be used in indoor navigation, these two estimation methods have some limitations [30] Both EKF and UKF have challenges in coping with non-Gaussian model problems and require known prior initial position information. A. INDOOR NAVIGATION BASED ON WALL CONSTRAINT USING PF The accumulation of PDR errors can lead to considerable uncertainty during the location estimation. PROPOSED PFMK METHOD The proposed particle filter with map restriction and unsupervised learning is intended to resolve a particle degradation while improving the navigation accuracy.
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