Calibration-Free Localizations and Applications on U-Care Cloud

2013 ◽  
Vol 5 (3) ◽  
pp. 65-74
Author(s):  
Shih-Lin Wu ◽  
Yu-Liang Yeh ◽  
Chia-Feng Lin
Keyword(s):  
Minimum Mean Square Error ◽  
Main Idea ◽  
Signal Strength ◽  
Location Estimation ◽  
Reference Points ◽  
Mobile Users ◽  
Average Error ◽  
Positioning System ◽  
Care Project ◽  
Radio Map

The key technique of the location-based service (LBS) is localization which is a kind of techniques for determining the location of mobile users (MU). One of the most common techniques for the location estimation of mobile users (MU) is the radio frequency (RF) site survey. The main idea of the technique is to build a signal strength model, called a radio map, in the off-line phase and to estimate the location of an MU by finding the best match from the radio map in the on-line phase. However, when signal strength values vary frequently due to the characteristics of environmental dynamics, the radio map will quickly become outdated, and recalibration requires considerable manual effort. A good positioning technique should be able to adapt to a dynamically changing environment. In this paper, the authors describe the design and implementation of a positioning system which can provide low-cost, but highly adaptable and precise positioning in the context of changeable radio environments. Instead of constructing a radio map, the authors use reference points (RP) installed in the localization area to continuously monitor changes in the environment. The authors then employ the minimum mean square error (MMSE) method to estimate the location of the MU. Experimental results show that the average error distance is about 3 meters. The positioning system has been implemented as a subsystem of the U-care project in the Chang Gung Health and Culture Village. From the results of questionnaire made by residents, the authors find that the most satisfaction system in the U-care project is the positioning and rescuing system because it can locate the exact position of a resident in need of emergency.

scholarly journals Practical Fingerprinting Localization for Indoor Positioning System by Using Beacons

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Santosh Subedi ◽  
Jae-Young Pyun
Keyword(s):  
Estimation Error ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Reference Points ◽  
Ultra Wideband ◽  
Positioning System ◽  
Localization Method ◽  
Weighted Centroid Localization ◽  
Time Required ◽  
Indoor Positioning System

Recent developments in the fields of smartphones and wireless communication technologies such as beacons, Wi-Fi, and ultra-wideband have made it possible to realize indoor positioning system (IPS) with a few meters of accuracy. In this paper, an improvement over traditional fingerprinting localization is proposed by combining it with weighted centroid localization (WCL). The proposed localization method reduces the total number of fingerprint reference points over the localization space, thus minimizing both the time required for reading radio frequency signals and the number of reference points needed during the fingerprinting learning process, which eventually makes the process less time-consuming. The proposed positioning has two major steps of operation. In the first step, we have realized fingerprinting that utilizes lightly populated reference points (RPs) and WCL individually. Using the location estimated at the first step, WCL is run again for the final location estimation. The proposed localization technique reduces the number of required fingerprint RPs by more than 40% compared to normal fingerprinting localization method with a similar localization estimation error.

scholarly journals Research and Application of Underground WLAN Adaptive Radio Fingerprint Database

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1182
Author(s):  
Jiansheng Qian ◽  
Mingzhi Song
Keyword(s):  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Reference Points ◽  
Normal Operation ◽  
Positioning System ◽  
Widespread Application ◽  
Radio Fingerprint ◽  
Fingerprint Database ◽  
User Location

Fingerprint positioning based on WiFi in coal mines has received much attention because of the widespread application of WiFi. Fingerprinting techniques have developed rapidly due to the efforts of many researchers. However, the off-line construction of the radio fingerprint database is a tedious and time-consuming process. When the underground environments change, it may be necessary to update the signal received signal strength indication (RSSI) of all reference points, which will affect the normal working of a personnel positioning system. To solve this problem, an adaptive construction and update method based on a quantum-behaved particle swarm optimization–user-location trajectory feedback (QPSO–ULTF) for a radio fingerprint database is proposed. The principle of ULTF is that the mobile terminal records and uploads the related dataset in the process of user’s walking, and it forms the user-location track with RSSI through the analysis and processing of the positioning system server. QPSO algorithm is used for the optimal radio fingerprint match between the RSSI of the access point (AP) contained in the dataset of user-location track and the calibration samples to achieve the adaptive generation and update of the radio fingerprint samples. The experimental results show that the radio fingerprint database generated by the QPSO–ULTF is similar to the traditional radio fingerprint database in the statistical distribution characteristics of the signal received signal strength (RSS) at each reference point. Therefore, the adaptive radio fingerprint database can replace the traditional radio fingerprint database. The comparable results of well-known traditional positioning methods demonstrate that the radio fingerprint database generated or updated by the QPSO–ULTF has a good positioning effect, which can ensure the normal operation of a personnel positioning system.

scholarly journals Accurate indoor positioning system based on modify nearest point technique

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).

scholarly journals Real-time RSS-based positioning system using neural network algorithm

2021 ◽  
Vol 22 (3) ◽  
pp. 1601
Author(s):  
Safae El Abkari ◽  
Jamal El Mhamdi ◽  
El Hassan El Abkari
Keyword(s):  
Neural Network ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Grid Size ◽  
Average Error ◽  
Positioning System ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Neural Network Algorithm ◽  
Locating System

Locating services have come under the spotlight in recent years in various applications. However, locating methods that use received signal strength have low accuracy due to signal fluctuations. For this purpose, we present a Wi-Fi based locating system using artificial neural network to enhance the positioning process performances. We optimized the Levenberg Marquardt algorithm to propose the better configuration of the multi-layer time-delay perception neural network. We achieved an average error of 10.3 centimeters with a grid of 0.4 meter in four tests. Yet, due to the instability of the received signal strength RSS-based locating systems present a limitation in the resolution finesse that depends on the grid size.

scholarly journals Localization Reliability Improvement Using Deep Gaussian Process Regression Model

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4164 ◽  
Author(s):  
Fei Teng ◽  
Wenyuan Tao ◽  
Chung-Ming Own
Keyword(s):  
Gaussian Process ◽  
Dynamic Environment ◽  
Gaussian Process Regression ◽  
Computation Time ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Reference Points ◽  
Nonparametric Model ◽  
Positioning System ◽  
Positioning Accuracy

With the widespread use of the Global Positioning System, indoor positioning technology has attracted increasing attention. Many systems with distinct deployment costs and positioning accuracies have been developed over the past decade for indoor positioning. The method that is based on received signal strength (RSS) is the most widely used. However, manually measuring RSS signal values to build a fingerprint database is costly and time-consuming, and it is impractical in a dynamic environment with a large positioning area. In this study, we propose an indoor positioning system that is based on the deep Gaussian process regression (DGPR) model. This model is a nonparametric model and it only needs to measure part of the reference points, thus reducing the time and cost required for data collection. The model converts the RSS values into four types of characterizing values as input data and then predicts the position coordinates using DGPR. Finally, after reinforcement learning, the position coordinates are optimized. The authors conducted several experiments on a simulated environment by MATLAB and physical environments at Tianjin University. The experiments examined different environments, different kernels, and positioning accuracy. The results showed that the proposed method could not only retain the positioning accuracy, but also save the computation time that is required for location estimation.

scholarly journals Fast Construction of the Radio Map Based on the Improved Low-Rank Matrix Completion and Recovery Method for an Indoor Positioning System

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhuang Wang ◽  
Liye Zhang ◽  
Qun Kong ◽  
Kangtao Wang
Keyword(s):  
Matrix Completion ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Low Rank ◽  
Positioning System ◽  
Rank Matrix ◽  
Low Rank Matrix Completion ◽  
Low Rank Matrix ◽  
Indoor Positioning System ◽  
Radio Map

With the development of information technology, indoor positioning technology has been rapidly evolving. Due to the advantages of high positioning accuracy, low cost, and wide coverage simultaneously, received signal strength- (RSS-) based WLAN indoor positioning technology has become one of the mainstream technologies. A radio map is the basis for the realization of the WLAN positioning system. However, by reasons of the huge workload of RSS collection, the instability of wireless signal strength, and the disappearance of signals caused by the occlusion of people and objects, the construction of a radio map is time-consuming and inefficient. In order to rapidly deploy the WLAN indoor positioning system, an improved low-rank matrix completion method is proposed to construct the radio map. Firstly, we evenly arrange a small number of reference points (RP) in the positioning area and collect RSS data on the RP to construct the radio map. Then, the low-rank matrix completion method is used to fill a small amount of data in the radio map into a complete database. The Frobenius parameter (F-parameter) is introduced into the traditional low-rank matrix completion model to control the instability of the model solution when filling the data. To solve the noise problem caused by environment and equipment, a low-rank matrix recovery algorithm is used to eliminate noise. The experimental results show that the improved algorithm achieves the expected goal.

scholarly journals A Novel Hybrid NN-ABPE-Based Calibration Method for Improving Accuracy of Lateration Positioning System

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8204
Author(s):  
Milica Petrović ◽  
Maciej Ciężkowski ◽  
Sławomir Romaniuk ◽  
Adam Wolniakowski ◽  
Zoran Miljković
Keyword(s):  
Neural Network ◽  
Main Idea ◽  
Signal Propagation ◽  
Calibration Method ◽  
Position Estimation ◽  
Reference Points ◽  
Positioning System ◽  
Feed Forward Neural Network ◽  
Distance Measurements ◽  
Positioning Systems

Positioning systems based on the lateration method utilize distance measurements and the knowledge of the location of the beacons to estimate the position of the target object. Although most of the global positioning techniques rely on beacons whose locations are known a priori, miscellaneous factors and disturbances such as obstacles, reflections, signal propagation speed, the orientation of antennas, measurement offsets of the beacons hardware, electromagnetic noise, or delays can affect the measurement accuracy. In this paper, we propose a novel hybrid calibration method based on Neural Networks (NN) and Apparent Beacon Position Estimation (ABPE) to improve the accuracy of a lateration positioning system. The main idea of the proposed method is to use a two-step position correction pipeline that first performs the ABPE step to estimate the perceived positions of the beacons that are used in the standard position estimation algorithm and then corrects these initial estimates by filtering them with a multi-layer feed-forward neural network in the second step. In order to find an optimal neural network, 16 NN architectures with 10 learning algorithms and 12 different activation functions for hidden layers were implemented and tested in the MATLAB environment. The best training outcomes for NNs were then employed in two real-world indoor scenarios: without and with obstacles. With the aim to validate the proposed methodology in a scenario where a fast set-up of the system is desired, we tested eight different uniform sampling patterns to establish the influence of the number of the training samples on the accuracy of the system. The experimental results show that the proposed hybrid NN-ABPE method can achieve a high level of accuracy even in scenarios when a small number of calibration reference points are measured.

scholarly journals An Accurate Visual-Inertial Integrated Geo-Tagging Method for Crowdsourcing-Based Indoor Localization

2019 ◽  
Vol 11 (16) ◽  
pp. 1912 ◽  
Author(s):  
Tao Liu ◽  
Xing Zhang ◽  
Qingquan Li ◽  
Zhixiang Fang ◽  
Nadeem Tahir
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Reference Points ◽  
External Information ◽  
Calibration Error ◽  
K Nearest Neighbor ◽  
Initial Location ◽  
Radio Map

One of the unavoidable bottlenecks in the public application of passive signal (e.g., received signal strength, magnetic) fingerprinting-based indoor localization technologies is the extensive human effort that is required to construct and update database for indoor positioning. In this paper, we propose an accurate visual-inertial integrated geo-tagging method that can be used to collect fingerprints and construct the radio map by exploiting the crowdsourced trajectory of smartphone users. By integrating multisource information from the smartphone sensors (e.g., camera, accelerometer, and gyroscope), this system can accurately reconstruct the geometry of trajectories. An algorithm is proposed to estimate the spatial location of trajectories in the reference coordinate system and construct the radio map and geo-tagged image database for indoor positioning. With the help of several initial reference points, this algorithm can be implemented in an unknown indoor environment without any prior knowledge of the floorplan or the initial location of crowdsourced trajectories. The experimental results show that the average calibration error of the fingerprints is 0.67 m. A weighted k-nearest neighbor method (without any optimization) and the image matching method are used to evaluate the performance of constructed multisource database. The average localization error of received signal strength (RSS) based indoor positioning and image based positioning are 3.2 m and 1.2 m, respectively, showing that the quality of the constructed indoor radio map is at the same level as those that were constructed by site surveying. Compared with the traditional site survey based positioning cost, this system can greatly reduce the human labor cost, with the least external information.

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