WiFi based indoor localization with adaptive motion model using smartphone motion sensors

2014 ◽  
Author(s):  
Xiang He ◽  
Jia Li ◽  
Daniel Aloi
Keyword(s):  
Indoor Localization ◽  
Motion Sensors ◽  
Motion Model ◽  
Adaptive Motion

Adaptive Motion Model for Human Tracking Using Particle Filter

2010 ◽  
Author(s):  
Mohammad Hossein Ghaeminia ◽  
Amir Hossein Shabani ◽  
Shahryar Baradaran Shokouhi
Keyword(s):  
Particle Filter ◽  
Human Tracking ◽  
Motion Model ◽  
Adaptive Motion

Structural-parametric synthesis of the tracking filter based on decomposition by the target functional with adaptation to trajectory disturbances

2021 ◽  
Author(s):  
A.A. Kostoglotov ◽  
A.S. Penkov ◽  
S.V. Lazarenko
Keyword(s):  
Numerical Modeling ◽  
State Assessment ◽  
Estimation Accuracy ◽  
Motion Model ◽  
Parametric Synthesis ◽  
Adaptive Motion ◽  
Accuracy Criterion ◽  
Dynamic Errors ◽  
Tracking Filter ◽  
Kinematic Motion

Traditional Kalman-type tracking filters are based on a kinematic motion model, which leads to the occurrence of dynamic errors, which significantly increase during target maneuvering. One of the solutions to this problem is to develop a model of motion dynamics with the ability to adapt its structure to external influences. It is shown that the use of a dynamic model of motion in the filter, which takes into account the inertia of the target and the forces acting on it, makes it possible to significantly increase the efficiency of the state assessment. Purpose is to development of an algorithm for assessing the position of a maneuvering object, effective in terms of accuracy criterion. The use of an adaptive motion model as part of the filter provides an increase in the estimation accuracy in comparison with the classical Kalman filter, which is confirmed by the performed numerical modeling.

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 Pedestrian Motion Learning Based Indoor WLAN Localization via Spatial Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaolong Yang ◽  
Yanmeng Wang ◽  
Mu Zhou ◽  
Yiyao Liu
Keyword(s):  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Spatial Clustering ◽  
Local Area ◽  
Labor Cost ◽  
Location Based Services ◽  
Area Network ◽  
Motion Sensors ◽  
Motion Learning

Applications on Location Based Services (LBSs) have driven the increasing demand for indoor localization technology. The conventional location fingerprinting based localization involves heavy time and labor cost for database construction, while the well-known Simultaneous Localization and Mapping (SLAM) technique requires assistant motion sensors as well as complicated data fusion algorithms. To solve the above problems, a new pedestrian motion learning based indoor Wireless Local Area Network (WLAN) localization approach is proposed in this paper to achieve satisfactory LBS without the demand for location calibration or motion sensors. First of all, the concept of pedestrian motion learning is adopted to construct users’ motion paths in the target environment. Second, based on the timestamp relation of the collected Received Signal Strength (RSS) sequences, the RSS segments are constructed to obtain the signal clusters with the newly defined high-dimensional linear distance. Third, the PageRank algorithm is performed to establish the hotspot mapping relations between the physical and signal spaces which are then used to localize the target. Finally, the experimental results show that the proposed approach can effectively estimate the target’s locations and analyze users’ motion preference in indoor environment.

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