On Building an Indoor Radio Map from Crowdsourced Samples with Annotation Errors

2017 ◽  
Author(s):  
Yanzhen Ye ◽  
Bang Wang
Keyword(s):  
Indoor Radio ◽  
Radio Map ◽  
Annotation Errors

scholarly journals Indoor Radio Map Construction Based on Position Adjustment and Equipment Calibration

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2818
Author(s):  
Ruolin Guo ◽  
Danyang Qin ◽  
Min Zhao ◽  
Xinxin Wang
Keyword(s):  
Particle Filtering ◽  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Dead Reckoning ◽  
Local Area ◽  
Area Network ◽  
Map Construction ◽  
Indoor Radio ◽  
Radio Map

The crowdsourcing-based wireless local area network (WLAN) indoor localization system has been widely promoted for the effective reduction of the workload from the offline phase data collection while constructing radio maps. Aiming at the problem of the diverse terminal devices and the inaccurate location annotation of the crowdsourced samples, which will result in the construction of the wrong radio map, an effective indoor radio map construction scheme (RMPAEC) is proposed based on position adjustment and equipment calibration. The RMPAEC consists of three main modules: terminal equipment calibration, pedestrian dead reckoning (PDR) estimated position adjustment, and fingerprint amendment. A position adjustment algorithm based on selective particle filtering is used by RMPAEC to reduce the cumulative error in PDR tracking. Moreover, an inter-device calibration algorithm is put forward based on receiver pattern analysis to obtain a device-independent grid fingerprint. The experimental results demonstrate that the proposed solution achieves higher localization accuracy than the peer schemes, and it possesses good effectiveness at the same time.

Adversarial Learning-Enabled Automatic WiFi Indoor Radio Map Construction and Adaptation With Mobile Robot

2020 ◽  
Vol 7 (8) ◽  
pp. 6946-6954 ◽  
Author(s):  
Han Zou ◽  
Chun-Lin Chen ◽  
Maoxun Li ◽  
Jianfei Yang ◽  
Yuxun Zhou ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Adversarial Learning ◽  
Map Construction ◽  
Indoor Radio ◽  
Radio Map

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

2012 ◽  
Author(s):  
Philipp M. Scholl ◽  
Stefan Kohlbrecher ◽  
Vinay Sachidananda ◽  
Kristof Van Laerhoven
Keyword(s):  
Map Building ◽  
Indoor Radio ◽  
Radio Map ◽  
Localization Systems

A building method of location fingerprint in indoor Radio-map

2017 ◽  
Vol 12 (4) ◽  
pp. 408
Author(s):  
Erhao Jing ◽  
Yubin Xu ◽  
Qian Xu
Keyword(s):  
Indoor Radio ◽  
Radio Map

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

Sensors ◽  
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.

scholarly journals A Visual-Based Approach for Indoor Radio Map Construction Using Smartphones

Sensors ◽  
2017 ◽  
Vol 17 (8) ◽  
pp. 1790 ◽  
Author(s):  
Tao Liu ◽  
Xing Zhang ◽  
Qingquan Li ◽  
Zhixiang Fang
Keyword(s):  
Map Construction ◽  
Indoor Radio ◽  
Radio Map

DeepMap: Deep Gaussian Process for Indoor Radio Map Construction and Location Estimation

2018 ◽  
Author(s):  
Xiangyu Wang ◽  
Xuyu Wang ◽  
Shiwen Mao ◽  
Jian Zhang ◽  
Senthilkumar C. G. Periaswamy ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Location Estimation ◽  
Map Construction ◽  
Indoor Radio ◽  
Radio Map
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