Abstract
The hierarchical-based structure is recognized as a favorable structure for wireless local area network (WLAN) positioning. It is comprised of two positioning phases: the coarse localization phase and the fine localization phase. In the coarse localization phase, the users’ positions are firstly narrowed down to smaller regions or clusters. Then, a fingerprint matching algorithm is adopted to estimate the users’ positions within the clusters during the fine localization phase. Currently the clustering strategies in the coarse localization phase can be divided into received signal strength (RSS) clustering and 3D clustering. And the commonly seen positioning algorithms in the fine localization phase include k nearest neighbors (kNN), kernel based and compressive sensing-based. This paper proposed an improved WLAN positioning method using the combination: 3D clustering for the coarse localization phase and the compressive sensing-based fine localization. The method have three favorable features: (1) By using the previously estimated positions to define the sub-reference fingerprinting map (RFM) in the first coarse localization phase, the method can adopt the prior information that the users’ positions are continuous during walking to improve positioning accuracy. (2) The compressive sensing theory is adopted in the fine localization phase, where the positioning problem is transformed to a signal reconstruction problem. This again can improve the positioning accuracy. (3) The second coarse localization phase is added to avoid the global fingerprint matching in traditional 3D clustering-based methods when the stuck-in-small-area problem is encountered, thus, sufficiently lowered the maximum positioning latency. The proposed improved hierarchical WLAN positioning method is compared with two traditional methods during the experiments section. The resulting positioning accuracy and positioning latency have shown that the performance of the proposed method has better performance in both aspects.
Highlights
Each time a position estimation is available from fine localization, we find the grids which are close to the previous position estimations and form the sub-reference fingerprinting map (RFM)
From the comparisons between method 1, 2, and 3 or method 4, 5 and 6, we can see that for the performance of fingerprinting algorithms, the compressive sensing algorithm is better than kernel based, which again is better than k nearest neighbors (kNN)
This paper have studied the different combinations of the mentioned clustering strategies and positioning algorithms
Summary
Location is the basis for a large amount of smart applications, e.g., location-based service (LBS). A problem is raised here that large errors from current estimation will affect the accuracy of subsequent estimates because the potential clusters may be stuck in a small area This is solved in [10] by adding a global search process if the newly collected fingerprints are significantly different from those in the potential clusters. This can greatly improve the positioning accuracy, global search may introduce significant latency. In the coarse localization phase, the previously estimated position is adopted to find the potential clusters as sub-RFM. Experiments are carried out and show that the proposed method is better than many existing methods in terms of accuracy
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