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.

scholarly journals Smartphone-Based Indoor Localization within a 13th Century Historic Building

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4095 ◽  
Author(s):  
Toni Fetzer ◽  
Frank Ebner ◽  
Markus Bullmann ◽  
Frank Deinzer ◽  
Marcin Grzegorzek
Keyword(s):  
Particle Filter ◽  
Indoor Localization ◽  
Weighted Average ◽  
Setup Time ◽  
13Th Century ◽  
Test Scenario ◽  
Optimization Scheme ◽  
Historic Building ◽  
Localization System ◽  
Access Points

Within this work we present an updated version of our indoor localization system for smartphones. The pedestrian’s position is given by means of recursive state estimation using a particle filter to incorporate different probabilistic sensor models. Our recently presented approximation scheme of the kernel density estimation allows to find an exact estimation of the current position, compared to classical methods like weighted-average. Absolute positioning information is given by a comparison between recent measurements of nearby access points and signal strength predictions. Instead of using time-consuming approaches like classic fingerprinting or measuring the exact positions of access points, we use an optimization scheme based on a set of reference measurements to estimate a corresponding model. This work provides three major contributions to the system. The most essential contribution is the novel state transition based on continuous walks along a navigation mesh, modeling only the building’s walkable areas. The localization system is further updated by incorporating a threshold-based activity recognition using barometer and accelerometer readings, allowing for continuous and smooth floor changes. Within the scope of this work, we tackle problems like multimodal densities and sample impoverishment (system gets stuck) by introducing different countermeasures. For the latter, a simplification of our previous solution is presented for the first time, which does not involve any major changes to the particle filter. The goal of this work is to propose a fast to deploy localization solution, that provides reasonable results in a high variety of situations. To stress our system, we have chosen a very challenging test scenario. All experiments were conducted within a 13th century historic building, formerly a convent and today a museum. The system is evaluated using 28 distinct measurement series on four different test walks, up to 310 m length and 10 min duration. It can be shown, that the here presented localization solution is able to provide a small positioning error, even under difficult conditions and faulty measurements. The introduced filtering methods allow for a real fail-safe system, while the optimization scheme enables an on-site setup-time of less then 120 min for the building’s 2500 m walkable area.

scholarly journals Indoor Navigation Using Particle Filter and Sensor Fusion

2012 ◽  
Vol 19 (2) ◽  
pp. 31-40
Author(s):  
Lukas Köping ◽  
Thomas Mühsam ◽  
Christian Ofenberg ◽  
Bernhard Czech ◽  
Michael Bernard ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Particle Filter ◽  
Indoor Localization ◽  
High Accuracy ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Sensor Data ◽  
Localization System ◽  
Multiple Sensor ◽  
Large Building

Abstract In this paper we present an indoor localization system based on particle filter and multiple sensor data like acceleration, angular velocity and compass data. With this approach we tackle the problem of documentation on large building yards during the construction phase. Due to the circumstances of such an environment we cannot rely on any data from GPS, Wi-Fi or RFID. Moreover this work should serve us as a first step towards an all-in-one navigation system for mobile devices. Our experimental results show that we can achieve high accuracy in position estimation.

scholarly journals An INS/Floor-Plan Indoor Localization System Using the Firefly Particle Filter

2018 ◽  
Vol 7 (8) ◽  
pp. 324 ◽  
Author(s):  
Jian Chen ◽  
Gang Ou ◽  
Ao Peng ◽  
Lingxiang Zheng ◽  
Jianghong Shi
Keyword(s):  
Particle Filter ◽  
Indoor Localization ◽  
Step Length ◽  
Location Based Services ◽  
Floor Plan ◽  
Localization System ◽  
The Core ◽  
Step Counting ◽  
Performance Results ◽  
Particle Impoverishment

Location-based services for smartphones are becoming more and more popular. The core of location-based services is how to estimate a user’s location. An INS/floor-plan indoor localization system, using the Firefly Particle Filter (FPF), is proposed to estimate a user’s location. INS includes an attitude angle module, a step length module and a step counting module. In the step length module, we propose a hybrid step length model. The proposed step length algorithm reasonably calculates a user’s step length. Because of sensor deviation, non-orthogonality and the user’s jitter, the main bottleneck for INS is that the error grows over time. To reduce the cumulative error, we design cascade filters including the Kalman Filter (KF) and FPF. To a certain extent, KF reduces velocity error and heading drift. On the other hand, the firefly algorithm is used to solve the particle impoverishment problem. Considering that a user may not cross an obstacle, the proposed particle filter is proposed to improve positioning performance. Results show that the average positioning error in walking experiments is 2.14 m.

scholarly journals SENSOR BASED ALGORITHMS FOR DEAD RECKONING: A COMPARISON OF TWO APPROACHES FOR SMARTPHONES

2014 ◽  
pp. 50-60
Author(s):  
Daniel Caspari ◽  
Mircea Strutu ◽  
Lucas Riedhammer ◽  
Uwe Grossmann
Keyword(s):  
Patient Monitoring ◽  
Indoor Localization ◽  
Real Life ◽  
Dead Reckoning ◽  
Sensor Data ◽  
Localization System ◽  
Starting Point ◽  
Smartphone Technology ◽  
User Friendly ◽  
Smartphone Sensor

The implementation of a reliable indoor localization system can be the starting point for a variety of much desired applications. Either for efficient patient monitoring inside a hospital or as an automatic guide inside a museum, a working localization solution can be useful. Smartphone technology represents a powerful and user friendly tool in order to achieve adequate indoor positioning. This paper explores the potential of using smartphone sensor data (accelerometer and compass) in order to track the location of the person holding the device using dead reckoning algorithms. Two different approaches are under scrutiny in order to assess their performance in different real life inspired scenarios.

Design of a Bell-Shaped Ultra Wideband Antenna for Indoor Localization System

2015 ◽  
Vol 5 (6) ◽  
pp. 323 ◽  
Author(s):  
Nadia Ghariani ◽  
Mohamed Salah Karoui ◽  
Mondher Chaoui ◽  
Mongi Lahiani ◽  
Hamadi Ghariani
Keyword(s):  
Indoor Localization ◽  
Ultra Wideband ◽  
Localization System ◽  
Wideband Antenna

scholarly journals An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 574
Author(s):  
Chendong Xu ◽  
Weigang Wang ◽  
Yunwei Zhang ◽  
Jie Qin ◽  
Shujuan Yu ◽  
...  
Keyword(s):  
Channel State Information ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Location Based Services ◽  
Channel State ◽  
State Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Increasing Demand ◽  
Degradation Problem

With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

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