With the rapid development of WIFI technology, WIFI-based indoor positioning technology has been widely studied for location-based services. To solve the problems related to the signal strength database adopted in the widely used fingerprint positioning technology, we first introduce a new system framework in this paper, which includes a modified AP firmware and some cheap self-made WIFI sensor anchors. The periodically scanned reports regarding the neighboring APs and sensor anchors are sent to the positioning server and serve as the calibration points. Besides the calculation of correlations between the target points and the neighboring calibration points, we take full advantage of the important but easily overlooked feature that the signal attenuation model varies in different regions in the regression algorithm to get more accurate results. Thus, a novel method called RSSI Geography Weighted Regression (RGWR) is proposed to solve the fingerprint database construction problem. The average error of all the calibration points’ self-localization results will help to make the final decision of whether the database is the latest or has to be updated automatically. The effects of anchors on system performance are further researched to conclude that the anchors should be deployed at the locations that stand for the features of RSSI distributions. The proposed system is convenient for the establishment of practical positioning system and extensive experiments have been performed to validate that the proposed method is robust and manpower efficient.

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As the location based services develop, more and more researches have been focused on the positioning technologies in mobile networks. The long term evolution advanced (LTE-A) system, commercialized as the 4th generation (4G) mobile communication system, is based on the following key features: the orthogonal frequency division multiplexing (OFDM), the relay, the multiple input multiple outputs (MIMO), the carrier aggregation (CA), and the coordinated multipoint transmission and reception (CoMP). In this paper, the impact of these features on the existing positioning technology specified in the LTE-A standards is systematically investigated. Moreover, two approaches are proposed to take full advantage of these features in terms of positioning technologies and the key positioning parameters, including the reference signal time difference (RSTD) in the observed time difference of arrival (OTDOA) technology and user equipment receiving time subtracting transmitting time (UE Rx-Tx) in the enhanced cell identity (E-CID) technology.

Fingerprint-based Wi-Fi localization systems have become attractive for researchers in indoor location-based services. Due to the fluctuant characteristics of received signal strength (RSS) and the lack of the research on environmental factors affecting the signal propagation, the accuracy of the previous systems heavily relies on environmental conditions. In this chapter, we propose a novel multi-agent fusion algorithm which combines multiple classifiers. Unlike previous multi-classifier combination rule, the proposed approach considers the relativity among classifiers according to co-decision matrix. Experimental results show that the multi-classifier approach outperforms single classifier in the test environment with the average accuracy and standard deviations greatly improved in the test environment.