Using Inquiry-based Bluetooth RSSI Probability Distributions for Indoor Positioning

Fingerprinting is a common technique for indoor positioning using short range Radio Frequency (RF) technologies such as Wireless Location Area Network (WLAN) and Bluetooth (BT). It works in two phases: The first phase is a data training phase in which a radio map for the targeted area is generated in advance, while the second phase is the real-time location determination phase using the radio map. Considering the work amount for generating the radio map, only a few samples of the Radio Signal Strength Indicator (RSSI) are typically collected at each reference point. The limited samples are not able to represent the real signal distribution well in the conventional fingerprint approach such as in an occurrence-based solution. This paper presents a new solution using the Weibull function for approximating the Bluetooth signal strength distribution in the data training phase. This approach requires only a few RSSI samples to estimate the parameters of the Weibull distribution. Compared to the occurrence-based solution, the Weibull function utilizes the shape, shift, and scale parameters to describe the distribution over the entire RSSI domain. This study indicates that the reliability and accuracy of the fingerprint database is improved with the Weibull function approach. A Histogram Maximum Likelihood position estimation based on Bayesian theory is utilized in the positioning phase. The test results show that the fingerprinting solution using the Weibull probability distribution performs better than the occurrence-based fingerprint approach.