No abstract

Modern indoor positioning system services are important technologies that play vital roles in modern life, providing many services such as recruiting emergency healthcare providers and for security purposes. Several large companies, such as Microsoft, Apple, Nokia, and Google, have researched location-based services. Wireless indoor localization is key for pervasive computing applications and network optimization. Different approaches have been developed for this technique using WiFi signals. WiFi fingerprinting-based indoor localization has been widely used due to its simplicity, and algorithms that fingerprint WiFi signals at separate locations can achieve accuracy within a few meters. However, a major drawback of WiFi fingerprinting is the variance in received signal strength (RSS), as it fluctuates with time and changing environment. As the signal changes, so does the fingerprint database, which can change the distribution of the RSS (multimodal distribution). Thus, in this paper, we propose that symmetrical Hölder divergence, which is a statistical model of entropy that encapsulates both the skew Bhattacharyya divergence and Cauchy–Schwarz divergence that are closed-form formulas that can be used to measure the statistical dissimilarities between the same exponential family for the signals that have multivariate distributions. The Hölder divergence is asymmetric, so we used both left-sided and right-sided data so the centroid can be symmetrized to obtain the minimizer of the proposed algorithm. The experimental results showed that the symmetrized Hölder divergence consistently outperformed the traditional k nearest neighbor and probability neural network. In addition, with the proposed algorithm, the position error accuracy was about 1 m in buildings.

No abstract

No abstract

In the last two decades, the indoor positioning systems (IPS) have attracted many researchers because of the great importance in many pervasive applications. Different techniques have been developed for IPS, a method that fingerprints the Received Signal Strength (RSS) of WLAN at specific places that can obtain high accuracy of about one meter at the exact location. A large range of indoor navigation needs can be provided by using IPS, especially in unusual conditions such as being in large complex buildings or emergency healthcare needs. IPS can play a great role in other applications that needs tracking and observing such as for the elderly people or for security purpose. In this paper, a framework that incorporates the probabilistic neural network (PNN) with Jensen-Bregman Divergence (JBD) is proposed. To validate our algorithm, the results were compared with PNN and kNN nearest neighbor. Where implemented inside an academic building. The experiment results show that PNN-JBD achieves competitive performance comparing with traditional approaches.