Dynamic Radio Map Construction for WLAN Indoor Location

2011 ◽  
Author(s):  
Huimin Wang ◽  
Lin Ma ◽  
Yubin Xu ◽  
Zhian Deng
Keyword(s):  
Indoor Location ◽  
Map Construction ◽  
Radio Map

scholarly journals A Fast Radio Map Construction Method Merging Self-Adaptive Local Linear Embedding (LLE) and Graph-Based Label Propagation in WLAN Fingerprint Localization Systems

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 767 ◽  
Author(s):  
Yepeng Ni ◽  
Jianping Chai ◽  
Yan Wang ◽  
Weidong Fang
Keyword(s):  
Label Propagation ◽  
Positioning Accuracy ◽  
Local Linear Embedding ◽  
Map Construction ◽  
Local Linear ◽  
Linear Embedding ◽  
Fingerprint Database ◽  
Radio Map ◽  
Localization Systems ◽  
Self Adaptive

Indoor WLAN fingerprint localization systems have been widely applied due to the simplicity of implementation on various mobile devices, including smartphones. However, collecting received signal strength indication (RSSI) samples for the fingerprint database, named a radio map, is significantly labor-intensive and time-consuming. To solve the problem, this paper proposes a semi-supervised self-adaptive local linear embedding algorithm to build the radio map. First, this method uses the self-adaptive local linear embedding (SLLE) algorithm based on manifold learning to reduce the dimension of the high-dimensional RSSI samples and to extract a neighbor weight matrix. Secondly, a graph-based label propagation (GLP) algorithm is employed to build the radio map by semi-supervised learning from a large number of unlabeled RSSI samples to a few labeled RSSI samples. Finally, we propose a k self-adaptive neighbor weight (kSNW) algorithm, used for radio map construction in this paper, to realize online localization. The results of the experiments conducted in a real indoor environment show that the proposed method reduces the demand for large quantities of labeled samples and achieves good positioning accuracy. With only 25% labeled RSSI samples, our system can obtain positioning accuracy of more than 88%, within 3 m of localization errors.

scholarly journals Fast Radio Map Construction by using Adaptive Path Loss Model Interpolation in Large-Scale Building

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 712 ◽  
Author(s):  
Jingxue Bi ◽  
Yunjia Wang ◽  
Zengke Li ◽  
Shenglei Xu ◽  
Jiapeng Zhou ◽  
...  
Keyword(s):  
Small Area ◽  
Least Squares Method ◽  
Path Loss ◽  
Reference Points ◽  
Kriging Interpolation ◽  
Positioning Accuracy ◽  
Loss Model ◽  
Path Loss Model ◽  
Map Construction ◽  
Radio Map

The radio map construction is usually time-consuming and labor-sensitive in indoor fingerprinting localization. We propose a fast construction method by using an adaptive path loss model interpolation. Received signal strength (RSS) fingerprints are collected at sparse reference points by using multiple smartphones based on crowdsourcing. Then, the path loss model of an access point (AP) can be built with several reference points by the least squares method in a small area. Afterwards, the RSS value can be calculated based on the constructed model and corresponding AP’s location. In the small area, all models of detectable APs can be built. The corresponding RSS values can be estimated at each interpolated point for forming the interpolated fingerprints considering RSS loss, RSS noise and RSS threshold. Through combining all interpolated and sparse reference fingerprints, the radio map of the whole area can be obtained. Experiments are conducted in corridors with a length of 211 m. To evaluate the performance of RSS estimation and positioning accuracy, inverse distance weighted and Kriging interpolation methods are introduced for comparing with the proposed method. Experimental results show that our proposed method can achieve the same positioning accuracy as complete manual radio map even with the interval of 9.6 m, reducing 85% efforts and time of construction.

scholarly journals Joint Indoor Localization and Radio Map Construction with Limited Deployment Load

2015 ◽  
Vol 14 (5) ◽  
pp. 1031-1043 ◽  
Author(s):  
Sameh Sorour ◽  
Yves Lostanlen ◽  
Shahrokh Valaee ◽  
Khaqan Majeed
Keyword(s):  
Indoor Localization ◽  
Map Construction ◽  
Radio Map
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