An Algorithm for Accurate and Robust Indoor Localization Based on Nonlinear Programming

The study of techniques to estimate the position of mobile devices with a high level of accuracy and robustness is essential to provide advanced location based services in indoor environments. An algorithm to enable mobile devices to estimate their positions in known indoor environments is proposed in this paper under the assumption that fixed anchor nodes are available at known locations. The proposed algorithm is specifically designed to be executed on the mobile device whose position is under investigation, and it allows the device to estimate its position within the environment by actively measuring distance estimates from the anchor nodes. In order to reduce the impact of the errors caused by the arrangement of the anchor nodes in the environment, the proposed algorithm first transforms the localization problem into an optimization problem, and then, it solves the derived optimization problem using techniques inspired by nonlinear programming. Experimental results obtained using ultra-wide band signaling are presented to assess the performance of the algorithm and to compare it with reference alternatives. The presented experimental results confirm that the proposed algorithm provides an increased level of accuracy and robustness with respect to two reference alternatives, regardless of the position of the anchor nodes.

Download Full-text

Indoor Localization System Using Wireless Sensor Network

Iraqi Journal of Computer Communication Control and System Engineering ◽

10.33103/uot.ijccce.18.1.3 ◽

2018 ◽

pp. 29-38

Keyword(s):

Indoor Localization ◽

Low Cost ◽

Wide Band ◽

Human Movement ◽

Indoor Positioning ◽

Positioning System ◽

Wireless System ◽

Anchor Nodes ◽

Indoor Positioning System ◽

Better Than

Building a precise low cost indoor positioning and navigation wireless system is a challenging task. The accuracy and cost should be taken together into account. Especially, when we need a system to be built in a harsh environment. In recent years, several researches have been implemented to build different indoor positioning system (IPS) types for human movement using wireless commercial sensors. The aim of this paper is to prove that it is not always the case that having a larger number of anchor nodes will increase the accuracy. Two and three anchor nodes of ultra-wide band with or without the commercial devices (DW 1000) could be implemented in this work to find the Localization of objects in different indoor positioning system, for which the results showed that sometimes three anchor nodes are better than two and vice versa. It depends on how to install the anchor nodes in an appropriate scenario that may allow utilizing a smaller number of anchors while maintaining the required accuracy and cost.

Download Full-text

A Novel Passive Indoor Localization Method by Fusion CSI Amplitude and Phase Information

Sensors ◽

10.3390/s19040875 ◽

2019 ◽

Vol 19 (4) ◽

pp. 875 ◽

Cited By ~ 10

Author(s):

Xiaochao Dang ◽

Xiong Si ◽

Zhanjun Hao ◽

Yaning Huang

Keyword(s):

Indoor Localization ◽

Rapid Development ◽

Location Based Services ◽

Signal Interference ◽

Indoor Environments ◽

Phase Information ◽

Localization Accuracy ◽

Localization Method ◽

Indoor Location ◽

Conference Room

With the rapid development of wireless network technology, wireless passive indoor localization has become an increasingly important technique that is widely used in indoor location-based services. Channel state information (CSI) can provide more detailed and specific subcarrier information, which has gained the attention of researchers and has become an emphasis in indoor localization technology. However, existing research has generally adopted amplitude information for eigenvalue calculations. There are few research studies that have used phase information from CSI signals for localization purposes. To eliminate the signal interference existing in indoor environments, we present a passive human indoor localization method named FapFi, which fuses CSI amplitude and phase information to fully utilize richer signal characteristics to find location. In the offline stage, we filter out redundant values and outliers in the CSI amplitude information and then process the CSI phase information. A fusion method is utilized to store the processed amplitude and phase information as a fingerprint database. The experimental data from two typical laboratory and conference room environments were gathered and analyzed. The extensive experimental results demonstrate that the proposed algorithm is more efficient than other algorithms in data processing and achieves decimeter-level localization accuracy.

Download Full-text

Interval arithmetic to support effective indoor positioning of software agents

Intelligenza Artificiale ◽

10.3233/ia-190042 ◽

2020 ◽

Vol 14 (1) ◽

pp. 59-73

Author(s):

Stefania Monica ◽

Federico Bergenti

Keyword(s):

Mobile Device ◽

Interval Arithmetic ◽

Indoor Environment ◽

Optimization Problem ◽

Sufficient Accuracy ◽

Location Based Services ◽

Geometric Algorithms ◽

Line Of Sight ◽

Indoor Environments ◽

Localization Problem

The provision of advanced location-based services in indoor environments is based on the possibility of estimating the positions of mobile devices with sufficient accuracy and robustness. An algorithm to allow a software agent hosted on a mobile device to estimate the position of its device in a known indoor environment is proposed under the ordinary assumption that fixed beacons are installed in the environment at known locations. Rather than making use of geometric considerations to estimate the position of the device, the proposed algorithm first transforms the localization problem into a related optimization problem, which is then solved by means of interval arithmetic to provide the agent with accurate and robust position estimates. The adopted approach solves a major problem that severely limits the accuracy of the position estimates that ordinary geometric algorithms provide when the beacons are positioned to maximize line-of-sight coverage. Experimental results confirm that the proposed algorithm provides position estimates that are independent of the positions of the beacons, and they show that the algorithm outperforms a well-known geometric algorithm.

Download Full-text

Indoor Positioning on Disparate Commercial Smartphones Using Wi-Fi Access Points Coverage Area

Sensors ◽

10.3390/s19194351 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4351 ◽

Cited By ~ 9

Author(s):

Ashraf ◽

Hur ◽

Park

Keyword(s):

Human Mobility ◽

Indoor Positioning ◽

Location Based Services ◽

Indoor Environments ◽

Coverage Area ◽

Positioning Accuracy ◽

Positioning Systems ◽

Access Points ◽

Intersection Area ◽

The Impact

The applications of location-based services require precise location information of a user both indoors and outdoors. Global positioning system’s reduced accuracy for indoor environments necessitated the initiation of Indoor Positioning Systems (IPSs). However, the development of an IPS which can determine the user’s position with heterogeneous smartphones in the same fashion is a challenging problem. The performance of Wi-Fi fingerprinting-based IPSs is degraded by many factors including shadowing, absorption, and interference caused by obstacles, human mobility, and body loss. Moreover, the use of various smartphones and different orientations of the very same smartphone can limit its positioning accuracy as well. As Wi-Fi fingerprinting is based on Received Signal Strength (RSS) vector, it is prone to dynamic intrinsic limitations of radio propagation, including changes over time, and far away locations having similar RSS vector. This article presents a Wi-Fi fingerprinting approach that exploits Wi-Fi Access Points (APs) coverage area and does not utilize the RSS vector. Using the concepts of APs coverage area uniqueness and coverage area overlap, the proposed approach calculates the user’s current position with the help of APs’ intersection area. The experimental results demonstrate that the device dependency can be mitigated by making the fingerprinting database with the proposed approach. The experiments performed at a public place proves that positioning accuracy can also be increased because the proposed approach performs well in dynamic environments with human mobility. The impact of human body loss is studied as well.

Download Full-text

DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier

Sensors ◽

10.3390/s20010133 ◽

2019 ◽

Vol 20 (1) ◽

pp. 133 ◽

Cited By ~ 6

Author(s):

Imran Ashraf ◽

Soojung Hur ◽

Sangjoon Park ◽

Yongwan Park

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Indoor Localization ◽

Ensemble Classifier ◽

Location Based Services ◽

Magnetic Data ◽

Neural Network Ensemble ◽

Localization Accuracy ◽

Mean Error ◽

The Impact

A quickly growing location-based services area has led to increased demand for indoor positioning and localization. Undoubtedly, Wi-Fi fingerprint-based localization is one of the promising indoor localization techniques, yet the variation of received signal strength is a major problem for accurate localization. Magnetic field-based localization has emerged as a new player and proved a potential indoor localization technology. However, one of its major limitations is degradation in localization accuracy when various smartphones are used. The localization performance is different from various smartphones even with the same localization technique. This research leverages the use of a deep neural network-based ensemble classifier to perform indoor localization with heterogeneous devices. The chief aim is to devise an approach that can achieve a similar localization accuracy using various smartphones. Features extracted from magnetic data of Galaxy S8 are fed into neural networks (NNs) for training. The experiments are performed with Galaxy S8, LG G6, LG G7, and Galaxy A8 smartphones to investigate the impact of device dependence on localization accuracy. Results demonstrate that NNs can play a significant role in mitigating the impact of device heterogeneity and increasing indoor localization accuracy. The proposed approach is able to achieve a localization accuracy of 2.64 m at 50% on four different devices. The mean error is 2.23 m, 2.52 m, 2.59 m, and 2.78 m for Galaxy S8, LG G6, LG G7, and Galaxy A8, respectively. Experiments on a publicly available magnetic dataset of Sony Xperia M2 using the proposed approach show a mean error of 2.84 m with a standard deviation of 2.24 m, while the error at 50% is 2.33 m. Furthermore, the impact of devices on various attitudes on the localization accuracy is investigated.

Download Full-text

Human Activity Recognition for Indoor Localization Using Smartphone Inertial Sensors

Sensors ◽

10.3390/s21186316 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6316

Author(s):

Dinis Moreira ◽

Marília Barandas ◽

Tiago Rocha ◽

Pedro Alves ◽

Ricardo Santos ◽

...

Keyword(s):

Activity Recognition ◽

Human Activity ◽

Human Activities ◽

Short Term Memory ◽

Indoor Localization ◽

Inertial Sensor ◽

Human Activity Recognition ◽

Location Based Services ◽

Sensor Data ◽

Indoor Environments

With the fast increase in the demand for location-based services and the proliferation of smartphones, the topic of indoor localization is attracting great interest. In indoor environments, users’ performed activities carry useful semantic information. These activities can then be used by indoor localization systems to confirm users’ current relative locations in a building. In this paper, we propose a deep-learning model based on a Convolutional Long Short-Term Memory (ConvLSTM) network to classify human activities within the indoor localization scenario using smartphone inertial sensor data. Results show that the proposed human activity recognition (HAR) model accurately identifies nine types of activities: not moving, walking, running, going up in an elevator, going down in an elevator, walking upstairs, walking downstairs, or going up and down a ramp. Moreover, predicted human activities were integrated within an existing indoor positioning system and evaluated in a multi-story building across several testing routes, with an average positioning error of 2.4 m. The results show that the inclusion of human activity information can reduce the overall localization error of the system and actively contribute to the better identification of floor transitions within a building. The conducted experiments demonstrated promising results and verified the effectiveness of using human activity-related information for indoor localization.

Download Full-text

Accurate indoor positioning system based on modify nearest point technique

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v12i2.pp1593-1601 ◽

2022 ◽

Vol 12 (2) ◽

pp. 1593

Author(s):

Omar Ibrahim Mustafa ◽

Hawraa Lateef Joey ◽

Noor Abd AlSalam ◽

Ibrahim Zeghaiton Chaloob

Keyword(s):

Indoor Localization ◽

Signal Strength ◽

Real Data ◽

Reference Points ◽

Positioning System ◽

Indoor Environments ◽

Nearest Point ◽

The Impact ◽

Indoor Positioning System

Wireless fidelity (Wi-Fi) is common technology for indoor environments that use to estimate required distances, to be used for indoor localization. Due to multiple source of noise and interference with other signal, the receive signal strength (RSS) measurements unstable. The impression about targets environments should be available to estimate accurate targets location. The Wi-Fi fingerprint technique is widely implemented to build database matching with real data, but the challenges are the way of collect accurate data to be the reference and the impact of different environments on signals measurements. In this paper, optimum system proposed based on modify nearest point (MNP). To implement the proposal, 78 points measured to be the reference points recorded in each environment around the targets. Also, the case study building is separated to 7 areas, where the segmentation of environments leads to ability of dynamic parameters assignments. Moreover, database based on optimum data collected at each time using 63 samples in each point and the average will be final measurements. Then, the nearest point into specific environment has been determined by compared with at least four points. The results show that the errors of indoor localization were less than (0.102 m).

Download Full-text

What the Smartphone Has Changed

Tap ◽

10.7551/mitpress/9780262036276.003.0003 ◽

2017 ◽

Author(s):

Anindya Ghose

Keyword(s):

Consumer Behavior ◽

Social Networking ◽

Mobile Devices ◽

Social Networking Site ◽

Location Based Services ◽

Internet Shopping ◽

Brick And Mortar ◽

The Impact ◽

The Way

This chapter discusses the impact of smartphones on consumer behavior. The proliferation of smartphones has changed the way people create and consume any kind of content. Whether streaming music on Apple Music or Spotify, uploading pictures and videos on a social networking site such as Snapchat or Instagram, or writing a review on Amazon, we now have the ability to create or consume content in a frictionless manner. During the early days of internet shopping, consumers used to browse online but make the actual purchase offline. Today, thanks to mobile devices, many consumers often browse in a brick-and-mortar shop and then complete the transaction online on their devices. Perhaps one of the biggest changes has been in mobile how businesses interact with their audiences. For instance, location-based services have enabled advertisers to alter content depending on what part of a city or even which block of a city the consumer is in.

Download Full-text

Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free

Sensors ◽

10.3390/s18082692 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2692 ◽

Cited By ~ 17

Author(s):

Yujin Chen ◽

Ruizhi Chen ◽

Mengyun Liu ◽

Aoran Xiao ◽

Dewen Wu ◽

...

Keyword(s):

Image Retrieval ◽

Pose Estimation ◽

Indoor Localization ◽

Location Based Services ◽

Visual Localization ◽

Indoor Environments ◽

Deep Convolutional Neural Networks ◽

Accurate Localization ◽

Visual Odometer ◽

Hybrid Indoor Localization Using WiFi and UWB Technologies

Electronics ◽

10.3390/electronics8030334 ◽

2019 ◽

Vol 8 (3) ◽

pp. 334 ◽

Cited By ~ 6

Author(s):

Stefania Monica ◽

Federico Bergenti

Keyword(s):

Indoor Localization ◽

Sufficient Accuracy ◽

Location Based Services ◽

Experimental Results ◽

Smart Devices ◽

Major Drawback ◽

Wireless Technologies ◽

Hybrid Approaches ◽

Accurate Localization

The interest in indoor localization has been increasing in the last few years because of the numerous important applications related to the pervasive diffusion of mobile smart devices that could benefit from localization. Various wireless technologies are in use to perform indoor localization, and, among them, WiFi and UWB technologies are appreciated when robust and accurate localization is required. The major advantage of WiFi technology is that it is ubiquitous, and therefore it can be used to support localization without the introduction of a specific infrastructure. The major drawback of WiFi technology is that it does not often ensure sufficient accuracy. On the contrary, indoor localization based on UWB technology guarantees higher accuracy with increased robustness, but it requires the use of UWB-enabled devices and the deployment of specific infrastructures made of UWB beacons. Experimental results on the synergic use of WiFi and UWB technologies for localization are presented in this paper to show that hybrid approaches can be used to effectively to increase the accuracy of WiFi-based localization. Actually, presented experimental results show that the use of a small number of UWB beacons together with an ordinary WiFi infrastructure is sufficient to significantly increase the accuracy of localization and to make WiFi-based localization adequate to implement relevant location-based services and applications.

Download Full-text