Wi-Fi and Motion Sensors Based Indoor Localization Combining ELM and Particle Filter

2015 ◽  
pp. 105-113 ◽  
Author(s):  
Xinlong Jiang ◽  
Yiqiang Chen ◽  
Junfa Liu ◽  
Yang Gu ◽  
Zhenyu Chen
Keyword(s):  
Particle Filter ◽  
Indoor Localization ◽  
Motion Sensors

scholarly journals Real-Time and Accurate Indoor Localization with Fusion Model of Wi-Fi Fingerprint and Motion Particle Filter

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Xinlong Jiang ◽  
Yiqiang Chen ◽  
Junfa Liu ◽  
Dingjun Liu ◽  
Yang Gu ◽  
...  
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Indoor Localization ◽  
High Accuracy ◽  
Motion Sensors ◽  
Fusion Model ◽  
Indoor Location ◽  
Smooth Tracking ◽  
Error Accumulation ◽  
Learning Machine

As the development of Indoor Location Based Service (Indoor LBS), a timely localization and smooth tracking with high accuracy are desperately needed. Unfortunately, any single method cannot meet the requirement of both high accuracy and real-time ability at the same time. In this paper, we propose a fusion location framework with Particle Filter using Wi-Fi signals and motion sensors. In this framework, we use Extreme Learning Machine (ELM) regression algorithm to predict position based on motion sensors and use Wi-Fi fingerprint location result to solve the error accumulation of motion sensors based location occasionally with Particle Filter. The experiments show that the trajectory is smoother as the real one than the traditional Wi-Fi fingerprint method.

scholarly journals An Improved Particle Filter Algorithm for Geomagnetic Indoor Positioning

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
He Huang ◽  
Wei Li ◽  
De An Luo ◽  
Dong Wei Qiu ◽  
Yang Gao
Keyword(s):  
Particle Filter ◽  
Indoor Localization ◽  
Iteration Process ◽  
Indoor Positioning ◽  
Matching Theory ◽  
Positioning Error ◽  
Matching Algorithm ◽  
Point Set ◽  
Particle Filter Algorithm ◽  
Error Constraint

Geomagnetic indoor positioning is an attractive indoor positioning technology due to its infrastructure-free feature. In the matching algorithm for geomagnetic indoor localization, the particle filter has been the most widely used. The algorithm however often suffers filtering divergence when there is continuous variation of the indoor magnetic distribution. The resampling step in the process of implementation would make the situation even worse, which directly lead to the loss of indoor positioning solution. Aiming at this problem, we have proposed an improved particle filter algorithm based on initial positioning error constraint, inspired by the Hausdorff distance measurement point set matching theory. Since the operating range of the particle filter cannot exceed the magnitude of the initial positioning error, it avoids the adverse effect of sampling particles with the same magnetic intensity but away from the target during the iteration process on the positioning system. The effectiveness and reliability of the improved algorithm are verified by experiments.

Particle filter and smoother for indoor localization

2013 ◽  
Author(s):  
Henri Nurminen ◽  
Anssi Ristimaki ◽  
Simo Ali-Loytty ◽  
Robert Piche
Keyword(s):  
Particle Filter ◽  
Indoor Localization

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

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 157 ◽  
Author(s):  
Michał R. Nowicki ◽  
Piotr Skrzypczyński
Keyword(s):  
Particle Filter ◽  
Indoor Localization ◽  
Historical Data ◽  
Dead Reckoning ◽  
Office Building ◽  
Localization System ◽  
Multiple Users ◽  
User Scenario ◽  
Single User

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.

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