Abstract
Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, lots of different devices are used in crowdsourcing system and less RSS values are collected by each device. Therefore, the crowdsourced RSS values are more erroneous and can result in significant localization errors. In order to eliminate the signal strength variations across diverse devices, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. After obtaining the uniform RSS values, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. As a result, the negative effect of the erroneous measurements could be mitigated. Since the AP locations need to be known in G-SSL algorithm, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.
Highlights
Indoor location-based services (LBS) such as indoor positioning, tracking and navigation, have been receiving a lot of attention in recent years [1,2]
Since the labels of all the vertices in the graph are necessary for sparse reconstruction of the graph weight matrix, Compressed Sensing (CS) method can only be used in the offline phase
After getting the uniform radio map, the RG-SSL method is proposed to improve the localization accuracy by smoothing the received signal strength (RSS) values and using label propagation to better estimate the radio map
Summary
Indoor location-based services (LBS) such as indoor positioning, tracking and navigation, have been receiving a lot of attention in recent years [1,2]. RSS values from different WiFi access points are measured at some known locations throughout the indoor area. The RSS value of an AP at a certain location can change over time due to a number of reasons including but not limited to multipath fading, shadowing, moving objects and people [10] To mitigate these RSS fluctuations, a large number of RSS measurements are collected at every reference point in the offline training phase. Based on the signal propagation model, the RSS difference between two locations is calculated with respect to the locations of RPs and APs. RG-SSL method is proposed to smoothen the radio map in the offline training phase.
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