Abstract
The main approach for a Wi-Fi indoor positioning system is based on the received signal strength (RSS) measurements, and the fingerprinting method is utilized to determine the user position by matching the RSS values with the pre-surveyed RSS database. To build a RSS fingerprint database is essential for an RSS based indoor positioning system, and building such a RSS fingerprint database requires lots of time and effort. As the range of the indoor environment becomes larger, labor is increased. To provide better indoor positioning services and to reduce the labor required for the establishment of the positioning system at the same time, an indoor positioning system with an appropriate spatial interpolation method is needed. In addition, the advantage of the RSS approach is that the signal strength decays as the transmission distance increases, and this signal propagation characteristic is applied to an interpolated database with the Kriging algorithm in this paper. Using the distribution of reference points (RPs) at measured points, the signal propagation model of the Wi-Fi access point (AP) in the building can be built and expressed as a function. The function, as the spatial structure of the environment, can create the RSS database quickly in different indoor environments. Thus, in this paper, a Wi-Fi indoor positioning system based on the Kriging fingerprinting method is developed. As shown in the experiment results, with a 72.2% probability, the error of the extended RSS database with Kriging is less than 3 dBm compared to the surveyed RSS database. Importantly, the positioning error of the developed Wi-Fi indoor positioning system with Kriging is reduced by 17.9% in average than that without Kriging.
Highlights
Today, various positioning technologies are used in many applications, and the users use several kinds of devices to get highly accurate location information
The K-weighted nearest neighbors (KWNN) algorithm is used to calculate the minimum distance between the received signal strength (RSS) measurement and the data pre-recorded in the database and is used to select several reference points (RPs) that are closely matched to the RSS measurement [9,17]
Since the goal of this paper is to reduce the measuring effort on real data at the training stage, 19 basic RPs of the 55 measured points are selected to estimate semivariograms and obtain influence range A and sill C
Summary
Various positioning technologies are used in many applications, and the users use several kinds of devices to get highly accurate location information. The main approach of the developed indoor positioning system is based on the received signal strength (RSS) observations. According to our previous ZigBee (IEEE 802.15.4) experimental results shown in [9,11,12], the Kriging algorithm is applied to yield a fingerprint database for an indoor positioning system and is able to maintain good positioning performance with a small number of RPs. in the experiments the ZigBee transmitters were installed at the pre-arranged locations to gain the optimal user positioning performance, and a regular grid formation over the indoor environment was used for the pre-arranged locations of the transmitters. To construct on IEEE 802.11 indoor positioning system that focuses on RSS database generation for the fingerprinting approach, the basic RSS database consists of an appropriately small number of measured RPs and is applied to create the semivariograms of the 9 APs in the building under investigation.
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