A Fast Radio Map Building Method Based on Floor Plan and Accelerometer

Noise Reduction for Radio Map Crowdsourcing Building in WLAN Indoor Localization System

10.21203/rs.3.rs-518323/v1 ◽

2021 ◽

Author(s):

liye zhang ◽

Zhuang Wang ◽

Xiaoliang Meng ◽

Chao Fang ◽

Cong Liu

Keyword(s):

Indoor Localization ◽

Sensor Data ◽

Mode Switching ◽

Map Building ◽

Position Information ◽

Localization Accuracy ◽

Localization System ◽

Straight Line ◽

Indoor Positioning System ◽

Radio Map

Abstract Recent years have witnessed a growing interest in using WLAN fingerprint-based method for indoor localization system because of its cost effectiveness and availability compared to other localization systems. In order to rapidly deploy WLAN indoor positioning system, the crowdsourcing method is applied to alternate the traditional deployment method. In this paper, we proposed a fast radio map building method utilizing the sensors inside the mobile device and the Multidimensional Scaling (MDS) method. The crowdsourcing method collects RSS and sensor data while the user is walking along a straight line and computes the position information using the sensor data. In order to reduces the noise in the location space of the radio map, the Short Term Fourier Transform (STFT) method is used to detect the usage mode switching to improve the step determination accuracy. When building a radio map, much fewer RSS values are needed using the crowdsourcing method compared to conventional methods, which lends greater influence to noises and erroneous measurements in RSS values. Accordingly, an imprecise radio map is built based on these imprecise RSS values. In order to acquire a smoother radio map and improve the localization accuracy, the MDS method is used to infer an optimal RSS value at each location by exploiting the correlation of RSS values at nearby locations. Experimental results show that the expected goal is achieved by the proposed method.

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Noise reduction for radio map crowdsourcing building in WLAN indoor localization system

EURASIP Journal on Advances in Signal Processing ◽

10.1186/s13634-021-00758-y ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Liye Zhang ◽

Zhuang Wang ◽

Xiaoliang Meng ◽

Chao Fang ◽

Cong Liu

Keyword(s):

Indoor Localization ◽

Sensor Data ◽

Mode Switching ◽

Map Building ◽

Position Information ◽

Localization Accuracy ◽

Localization System ◽

Straight Line ◽

Radio Map ◽

Localization Systems

AbstractRecent years have witnessed a growing interest in using WLAN fingerprint-based method for indoor localization system because of its cost-effectiveness and availability compared to other localization systems. In order to rapidly deploy WLAN indoor localization system, the crowdsourcing method is applied to alternate the traditional deployment method. In this paper, we proposed a fast radio map building method utilizing the sensors inside the mobile device and the Multidimensional Scaling (MDS) method. The crowdsourcing method collects RSS and sensor data while the user is walking along a straight line and computes the position information using the sensor data. In order to reduce the noise in the location space of the radio map, the short-term Fourier transform (STFT) method is used to detect the usage mode switching to improve the step determination accuracy. When building a radio map, much fewer RSS values are needed using the crowdsourcing method compared to conventional methods, which lends greater influence to noises and erroneous measurements in RSS values. Accordingly, an imprecise radio map is built based on these imprecise RSS values. In order to acquire a smoother radio map and improve the localization accuracy, the MDS method is used to infer an optimal RSS value at each location by exploiting the correlation of RSS values at nearby locations. Experimental results show that the expected goal is achieved by the proposed method.

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Fast indoor radio-map building for RSSI-based localization systems

2012 Ninth International Conference on Networked Sensing (INSS) ◽

10.1109/inss.2012.6240574 ◽

2012 ◽

Cited By ~ 12

Author(s):

Philipp M. Scholl ◽

Stefan Kohlbrecher ◽

Vinay Sachidananda ◽

Kristof Van Laerhoven

Keyword(s):

Map Building ◽

Indoor Radio ◽

Radio Map ◽

Localization Systems

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Access-Point Centered Window-Based Radio-Map Generation Network

Sensors ◽

10.3390/s21186107 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6107

Author(s):

Won-Yeol Kim ◽

Soo-Ho Tae ◽

Dong-Hoan Seo

Keyword(s):

Moving Objects ◽

Access Point ◽

Floor Plan ◽

Adversarial Learning ◽

Training Time ◽

Indoor Space ◽

Indoor Radio ◽

Map Generation ◽

Generation Network ◽

Radio Map

Fingerprinting is the term used to describe a common indoor radio-mapping positioning technology that tracks moving objects in real time. To use this, a substantial number of measurement processes and workflows are needed to generate a radio-map. Accordingly, to minimize costs and increase the usability of such radio-maps, this study proposes an access-point (AP)-centered window (APCW) radio-map generation network (RGN). The proposed technique extracts parts of a radio-map in the form of a window based on AP floor plan coordinates to shorten the training time while enhancing radio-map prediction accuracy. To provide robustness against changes in the location of the APs and to enhance the utilization of similar structures, the proposed RGN, which employs an adversarial learning method and uses the APCW as input, learns the indoor space in partitions and combines the radio-maps of each AP to generate a complete map. By comparing four learning models that use different data structures as input based on an actual building, the proposed radio-map learning model (i.e., APCW-based RGN) obtains the highest accuracy among all models tested, yielding a root-mean-square error value of 4.01 dBm.

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