An effective random statistical method for Indoor Positioning System using WiFi fingerprinting

Duong-Bao, Ninh; He, Jing; Trung, Vu Thanh; Huy, Dang Phuoc

doi:10.1016/j.future.2020.03.043

Cited by 32 publications

(13 citation statements)

References 19 publications

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“…The traditional algorithm is prone to matching errors due to the low specificity of the location fingerprint. Therefore, the collected data are normalized [21] (see 4.1.4) to improve the location fingerprint specificity and thus the matching accuracy. In the real-time localization phase, the robot acquires a real-time WiFi signal at an unknown point in the room.…”

Section: Methodsmentioning

confidence: 99%

Robot Indoor Positioning and Navigation Based on Improved WiFi Location Fingerprint Positioning Algorithm

Peng

2022

Wireless Communications and Mobile Computing

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In response to the traditional WiFi location fingerprint positioning algorithm still having a low positioning accuracy, which is difficult to meet the robot indoor positioning and navigation needs, a series of improvements are made to the traditional WiFi location fingerprint positioning algorithm, so that the positioning accuracy of the algorithm can be effectively improved. At the stage of building the location fingerprint library offline, WiFi signals are collected at each reference point by reducing the reference point spacing of the traditional location fingerprinting algorithm and then using different time period collection methods. The WiFi signal strength values are standardized using the standardization processing method to improve the specificity of traditional location fingerprinting. In the real-time localization stage, the WiFi signals collected from the unknown location points are averaged, and then, the fingerprint similarity calculation is performed using a matching method based on the magnitude of the Marxian distance as a similarity reference. In order to eliminate the location fingerprints that degrade the localization accuracy, an improved adaptive K -value WKNN algorithm is integrated at the end of the localization algorithm. The improved localization algorithm and the proposed raster-based navigation algorithm are validated in a fixed experimental environment. The experimental results show that the probability of the improved algorithm’s positioning error within 0.4 m is 49%, which is a 35% improvement over the conventional algorithm. Combining the improved positioning algorithm with our proposed grid-based navigation algorithm, the final navigation error probability within 0.8 m is 62%.

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Section: Methodsmentioning

confidence: 99%

Robot Indoor Positioning and Navigation Based on Improved WiFi Location Fingerprint Positioning Algorithm

Peng

2022

Wireless Communications and Mobile Computing

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“…In the actual locating stage, KNN matches the real-time collected Wi-Fi signal strength with the signal in the fingerprint library, finds the most similar sampling point of the fingerprint data, and uses the actual position of the sampling point as the estimated position of the test point to estimate the Wi-Fi fingerprint position [ 26 ]. KNN sorts the signal strength of the test point and the Euclidean distance of the signal strength in the location fingerprint database during calculation.…”

Section: Building Indoor Locating and Energy Consumptionmentioning

confidence: 99%

Applications of Nondominated Sorting Genetic Algorithm II Combined with WKNN Online Matching Algorithm in Building Indoor Optimization Design

Wang²,

Xiao

2022

Computational Intelligence and Neuroscience

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The present work aims to improve the comfort of architectural interior design and reduce indoor energy consumption. The Weight K-Nearest Neighborhood (WKNN) algorithm and Nondominated Sorting Genetic algorithm are proposed to locate and analyze the spatial location of indoor personnel and optimize the indoor energy consumption in combination with residential behavior. Firstly, the indoor human behavior data and energy-saving problems are analyzed based on residential behavior theory and architectural physics. The indoor positioning algorithm is proposed to identify the personnel activities to realize the optimization of indoor energy distribution. Secondly, mean filtering and cluster analysis are adopted to optimize sampling points’ data and fingerprint database to eliminate data noise. Besides, the WKNN algorithm is used for Wireless Fidelity (Wi-Fi) indoor location fingerprint location. Then, aiming at the multiobjective optimization problem of building indoor energy consumption, the Nondominated Sorting Genetic algorithm obtains the optimal solution of the model. Combined with the indoor location information of personnel, the indoor heating and cooling system is monitored and distributed to reduce the energy consumption in the building while ensuring the living comfort of personnel. The test and simulation results demonstrate that the mean filtering algorithm can solve the room’s fluctuation problem of Wi-Fi signals. The cluster analysis algorithm can eliminate the data noise of the fingerprint database and improve the positioning accuracy of the positioning algorithm. Moreover, the location result is independent of the number of nodes; the number of sampling points does not affect the location result. The positioning accuracy of the WKNN algorithm is 2 m, and the positioning error rate within 2 m is about 60%. Compared with other algorithms, the WKNN algorithm has better positioning accuracy and positioning stability. Therefore, the location algorithm designed here can be applied to indoor location optimization. This study provides a reference for optimizing buildings’ indoor positioning and energy consumption.

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“…The unique feature of these applications is using location information. Further, indoor location information has acquired a huge attention as the demand for Location-Based Services (LBS) users via smartphones [2]. There has been a huge of number of techniques and technologies to run LBS applications.…”

Section: Introductionmentioning

confidence: 99%

Novel Integration of Wi-Fi Signal and Magnetometer Sensor Measurements in Fingerprinting Technique for Indoors Smartphone positioning

2022

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Smartphones are becoming more widespread, and location-based services (LBS) have become one of the most important uses in people’s daily lives. While outdoor location is reasonably simple thanks to GNSS signals, however, indoor location is more problematic due to the lack of GNSS signals. As a result of the widespread deployment of alternative technologies such as wireless and sensors technologies, various studies on wireless-based indoor positioning have been conducted. However, each technology has its own limitations including multipath fading of wireless signals causes time-varying received signal strength as well as the accumulated error of the onboard sensors (i.e. sensor drift) resulting in poor localization accuracy. Motivated by these restrictions, this work integrates the applicability of two technologies for indoor positioning that are already available in smartphones by avoiding their limitation. The integration is based on fingerprinting-positioning technique by including magnetometer sensor measurements and WiFi signal strength. Android-based smartphones with low-cost sensors in real indoor scenarios are utilized to create a dataset and collect independent track tests to confirm results. The performance of different scenarios, such as Wi-Fi alone, magnetometer alone, and magnetometer-aided Wi-Fi, is compared. The experimental results show that the combination of magnetometer sensor and WiFi signal strength provides significant results in which leads to reducing the location error to 0.7224 meters.

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An effective random statistical method for Indoor Positioning System using WiFi fingerprinting

Cited by 32 publications

References 19 publications

Robot Indoor Positioning and Navigation Based on Improved WiFi Location Fingerprint Positioning Algorithm

Robot Indoor Positioning and Navigation Based on Improved WiFi Location Fingerprint Positioning Algorithm

Applications of Nondominated Sorting Genetic Algorithm II Combined with WKNN Online Matching Algorithm in Building Indoor Optimization Design

Novel Integration of Wi-Fi Signal and Magnetometer Sensor Measurements in Fingerprinting Technique for Indoors Smartphone positioning

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