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

Jian Chen; Gang Ou; Ao Peng; Lingxiang Zheng; Jianghong Shi

doi:10.3390/ijgi7080324

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7080324 ◽

2018 ◽

Vol 7 (8) ◽

pp. 324 ◽

Cited By ~ 4

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.

Download Full-text

DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

Sensors ◽

10.3390/s21062000 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2000

Author(s):

Marius Laska ◽

Jörg Blankenbach

Keyword(s):

Wireless Local Area Network ◽

Indoor Localization ◽

Local Area Network ◽

Success Probability ◽

Local Area ◽

Position Estimation ◽

Location Based Services ◽

Global Navigation Satellite Systems ◽

Area Network ◽

Floor Plan

Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user’s position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain.

Download Full-text

An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽

10.3390/e23050574 ◽

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.

Download Full-text

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

Sensors ◽

10.3390/s19010157 ◽

2019 ◽

Vol 19 (1) ◽

pp. 157 ◽

Cited By ~ 1

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.

Download Full-text

WiCLoc: A Novel CSI-based Fingerprint Localization System

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420580033 ◽

2019 ◽

Vol 34 (04) ◽

pp. 2058003

Author(s):

Lei Zhang ◽

Yanjun Hu ◽

Yafeng Liu ◽

Jiaxiang Li ◽

Enjie Ding

Keyword(s):

Nearest Neighbor ◽

Indoor Localization ◽

Rapid Development ◽

Location Based Services ◽

Smart Devices ◽

Fingerprint Matching ◽

Wifi Networks ◽

Localization System ◽

Exhibition Hall ◽

Localization Systems

With the rapid development of smart devices and WiFi networks, WiFi-based indoor localization is becoming increasingly important in location-based services. Among various localization techniques, the fingerprint-based method has attracted much interest due to its high accuracy and low equipment requirement. Traditional fingerprint-based indoor localization systems mostly obtain positioning by measuring the received signal strength indicator (RSSI). However, the RSSI is affected by environmental influences, thereby limiting the precision of positioning. Therefore, we propose a new indoor fingerprint localization system based on channel state information (CSI). We adopt a novel method, in which the amplitude and phase of the CSI are fused to generate fingerprints in the training phase and apply a weighted [Formula: see text]-nearest neighbor (KNN) algorithm for fingerprint matching during the estimation phase. The system is validated in an exhibition hall and laboratory and we also compare the results of the proposed system with those of two CSI-based and an RSSI-based fingerprint localization systems. The results show that the proposed system achieves a minimum mean distance error of 0.85[Formula: see text]m in the exhibition hall and 1.28[Formula: see text]m in the laboratory, outperforming the other systems.

Download Full-text

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

Sensors ◽

10.3390/s18124095 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4095 ◽

Cited By ~ 4

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.

Download Full-text

Indoor Navigation Using Particle Filter and Sensor Fusion

Annual of Navigation ◽

10.2478/v10367-012-0016-6 ◽

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.

Download Full-text

ZigBee-based indoor localization system with the personal dynamic positioning method and modified particle filter estimation

Analog Integrated Circuits and Signal Processing ◽

10.1007/s10470-017-0969-4 ◽

2017 ◽

Vol 92 (2) ◽

pp. 263-279 ◽

Cited By ~ 7

Author(s):

Mahmut Aykaç ◽

Ergun Erçelebi ◽

Noor Baha Aldin

Keyword(s):

Particle Filter ◽

Indoor Localization ◽

Dynamic Positioning ◽

Localization System ◽

Positioning Method

Download Full-text

Single UWB Anchor Aided PDR Heading and Step Length Correcting Indoor Localization System

IEEE Access ◽

10.1109/access.2021.3050405 ◽

2021 ◽

Vol 9 ◽

pp. 11511-11522

Author(s):

Keliu Long ◽

Chong Shen ◽

Chuan Tian ◽

Kun Zhang ◽

Uzair Aslam Bhatti ◽

...

Keyword(s):

Indoor Localization ◽

Step Length ◽

Localization System

Download Full-text

WiFi Fingerprint Localization for Emergency Response

Smart Technologies for Emergency Response and Disaster Management - Advances in Public Policy and Administration ◽

10.4018/978-1-5225-2575-2.ch003 ◽

2018 ◽

pp. 86-105 ◽

Cited By ~ 1

Author(s):

Yu Gu ◽

Min Peng ◽

Fuji Ren ◽

Jie Li

Keyword(s):

Radio Frequency ◽

Emergency Response ◽

Real World ◽

Indoor Localization ◽

State Of The Art ◽

Location Based Services ◽

Experimental Results ◽

Localization System ◽

Longest Common Subsequences ◽

Wifi Fingerprint

As a key enabler for diversified location-based services (LBSs) of pervasive computing, indoor WiFi fingerprint localization remains a hot topic for decades. For most of previous research, maintaining a stable Radio Frequency (RF) environment constitutes one implicit but basic assumption. However, there is little room for such assumption in real-world scenarios, especially for the emergency response. Therefore, we propose a novel solution (HED) for rapidly setting up an indoor localization system by harvesting from the bursting number of available wireless resources. Via extensive real-world experiments lasting for over 6 months, we show the superiority of our HED algorithm in terms of accuracy, complexity and stability over two state-of-the-art solutions that are also designed to resist the dynamics, i.e., FreeLoc and LCS (Longest Common Subsequences). Moreover, experimental results not only confirm the benefits brought by environmental dynamics, but also provide valuable investigations and hand-on experiences on the real-world localization system.

Download Full-text