Abstract
The major problem of Wi-Fi fingerprint-based positioning technology is the signal strength fingerprint database creation and maintenance. The significant temporal variation of received signal strength (RSS) is the main factor responsible for the positioning error. A probabilistic approach can be used, but the RSS distribution is required. The Gaussian distribution or an empirically-derived distribution (histogram) is typically used. However, these distributions are either not always correct or require a large amount of data for each reference point. Double peaks of the RSS distribution have been observed in experiments at some reference points. In this paper a new algorithm based on an improved double-peak Gaussian distribution is proposed. Kurtosis testing is used to decide if this new distribution, or the normal Gaussian distribution, should be applied. Test results show that the proposed algorithm can significantly improve the positioning accuracy, as well as reduce the workload of the off-line data training phase.
Highlights
One of the key issues for location based services (LBS) is the positioning technology, and the indoor positioning accuracy requirement is usually higher than that for outdoors [1,2]
For all individual test points (TPs) the performance of the improved double-peak Gaussian distribution (IDGD) is almost always the best, and overall performance is improved by about 40%, 20% and 21% compared with those based on the Gaussian, histogram and double-peak Gaussian distribution (DGD), respectively
The observation of double peaks of Wi-Fi signal strength has suggested the investigation of a new model known as the Double-peak Gaussian Distribution (DGD) to approximate the signal strength’s distribution
Summary
One of the key issues for location based services (LBS) is the positioning technology, and the indoor positioning accuracy requirement is usually higher than that for outdoors [1,2]. Indoor environments vary considerably from each other, which means one model may work well for a specific environment, but perform poorly in other situations. It is difficult, if not impossible, to accurately obtain distance measurements from SSs on a consistent basis [6]. The more accurate the fingerprint database ( referred to as the “radio map”) created, the better the positioning accuracy that can be achieved. The probabilistic approach can provide better accuracy for Wi-Fi positioning. The rest of the paper is organised as follows: Section 2 gives some details of the probabilistic approach of fingerprinting technology and discusses the characteristics of RSS. 20% and has the potential to significantly reduce the labour costs for the training phase
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