Hybrid Location Estimation by Fusing WLAN Signals and Inertial Data

Personal indoor localization with smartphones is a well-researched area, with a number of approaches solving the problem separately for individual users. Most commonly, a particle filter is used to fuse information from dead reckoning and WiFi or Bluetooth adapters to provide an accurate location of the person holding a smartphone. Unfortunately, the existing solutions largely ignore the gains that emerge when a single localization system estimates locations of multiple users in the same environment. Approaches based on filtration maintain only estimates of the current poses of the users, marginalizing the historical data. Therefore, it is difficult to fuse data from multiple individual trajectories that are usually not perfectly synchronized in time. We propose a system that fuses the information from WiFi and dead reckoning employing the graph-based optimization, which is widely applied in robotics. The presented system can be used for localization of a single user, but the improvement is especially visible when this approach is extended to a multi-user scenario. The article presents a number of experiments performed with a smartphone inside an office building. These experiments demonstrate that graph-based optimization can be used as an efficient fusion mechanism to obtain accurate trajectory estimates both in the case of a single user and in a multi-user indoor localization system. The code of our system together with recorded dataset will be made available when the paper gets published.

Section: Related Workmentioning

confidence: 99%

A Multi-User Personal Indoor Localization System Employing Graph-Based Optimization

Nowicki

Skrzypczyński

2019

“…For stride detection, peaks of measured total acceleration are counted. For step length estimation, a one-parameter nonlinear model [ 7 , 38 ] is employed:

where

(or

) is the maximum (or minimum) vertical acceleration in a single step and K is a constant. An assumption is that the leg is a lever of fixed length while the foot is on the ground.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…An assumption is that the leg is a lever of fixed length while the foot is on the ground. Location tracking is based on the primary theory of dead reckoning [ 7 , 38 ], and it is implemented in the frame of EKF, as presented in [ 38 ].…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Thus, it is essential to improve the tracking performance of the PDR method. Although external techniques, such as indoor graph matching [ 1 , 2 , 3 ], WiFi localization [ 3 , 4 , 5 , 6 , 7 ], visible light positioning [ 8 ] etc. have been considered to assist PDR, it is crucial to enhance its own performance in several aspects, such as speed estimation, heading determination and position calculation, the errors of which can propagate and can result in fast divergence of the localization performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering

Xia

Geng

2018

Self Cite

Pedestrian dead reckoning (PDR) using smart phone-embedded micro-electro-mechanical system (MEMS) sensors plays a key role in ubiquitous localization indoors and outdoors. However, as a relative localization method, it suffers from the problem of error accumulation which prevents it from long term independent running. Heading estimation error is one of the main location error sources, and therefore, in order to improve the location tracking performance of the PDR method in complex environments, an approach based on robust adaptive Kalman filtering (RAKF) for estimating accurate headings is proposed. In our approach, outputs from gyroscope, accelerometer, and magnetometer sensors are fused using the solution of Kalman filtering (KF) that the heading measurements derived from accelerations and magnetic field data are used to correct the states integrated from angular rates. In order to identify and control measurement outliers, a maximum likelihood-type estimator (M-estimator)-based model is used. Moreover, an adaptive factor is applied to resist the negative effects of state model disturbances. Extensive experiments under static and dynamic conditions were conducted in indoor environments. The experimental results demonstrate the proposed approach provides more accurate heading estimates and supports more robust and dynamic adaptive location tracking, compared with methods based on conventional KF.

“…The current fusion algorithms mainly focus on deploying the Particle Filter (PF) [14,15], Kalman Filter (KF) [16], and the improved algorithm based on the previous two algorithms. The PF helps a lot in improving the localization accuracy, especially for the nonlinear and non-Gaussian systems, but it involves high computation cost.…”

Section: Introductionmentioning

confidence: 99%

Wi-Fi/MARG Integration for Indoor Pedestrian Localization

Tian

Jin

Zhou

et al. 2016

With the wide deployment of Wi-Fi networks, Wi-Fi based indoor localization systems that are deployed without any special hardware have caught significant attention and have become a currently practical technology. At the same time, the Magnetic, Angular Rate, and Gravity (MARG) sensors installed in commercial mobile devices can achieve highly-accurate localization in short time. Based on this, we design a novel indoor localization system by using built-in MARG sensors and a Wi-Fi module. The innovative contributions of this paper include the enhanced Pedestrian Dead Reckoning (PDR) and Wi-Fi localization approaches, and an Extended Kalman Particle Filter (EKPF) based fusion algorithm. A new Wi-Fi/MARG indoor localization system, including an Android based mobile client, a Web page for remote control, and a location server, is developed for real-time indoor pedestrian localization. The extensive experimental results show that the proposed system is featured with better localization performance, with the average error 0.85 m, than the one achieved by using the Wi-Fi module or MARG sensors solely.