An Improved WiFi Indoor Positioning Algorithm by Weighted Fusion

Abstract

The rapid development of mobile Internet has offered the opportunity for WiFi indoor positioning to come under the spotlight due to its low cost. However, nowadays the accuracy of WiFi indoor positioning cannot meet the demands of practical applications. To solve this problem, this paper proposes an improved WiFi indoor positioning algorithm by weighted fusion. The proposed algorithm is based on traditional location fingerprinting algorithms and consists of two stages: the offline acquisition and the online positioning. The offline acquisition process selects optimal parameters to complete the signal acquisition, and it forms a database of fingerprints by error classification and handling. To further improve the accuracy of positioning, the online positioning process first uses a pre-match method to select the candidate fingerprints to shorten the positioning time. After that, it uses the improved Euclidean distance and the improved joint probability to calculate two intermediate results, and further calculates the final result from these two intermediate results by weighted fusion. The improved Euclidean distance introduces the standard deviation of WiFi signal strength to smooth the WiFi signal fluctuation and the improved joint probability introduces the logarithmic calculation to reduce the difference between probability values. Comparing the proposed algorithm, the Euclidean distance based WKNN algorithm and the joint probability algorithm, the experimental results indicate that the proposed algorithm has higher positioning accuracy.

Development of a Web-Based Indoor Navigation System Using an Accelerometer and Gyroscope: A Case Study at The Faculty of Natural Sciences of Comenius University

“…Those properties are used to estimate the angles and distances to the known position of the signal emitters and calculate their approximate position. Triangulation requires at least three signal emitters, but generally the more emitters in the range of the device, the more accurate the estimation (Ma et al, 2015).…”

“…Depending on many factors such as their degree of accuracy, response time or financial demands, various methods can be used to determine the actual location of a device. Until now, many technologies have been developed and used for detection of a device in interiors, e.g., multisensor-based navigation; Wireless Fidelity (Wi-Fi) triangulation (Ma et al, 2015;Shandilya and Idate, 2015); Radio Frequency IDentification (RFID) used in conjunction with Near Field Communication (NFC) (Geng et al, 2014); ultrasound (Gualda et al, 2014); inertial systems (Woodman, 2007); etc. These technologies have been described in detail in many studies (Dardari et al, 2015;Gu et al, 2009;Jeon et al, 2015;Koyuncu and Yang, 2010;Mautz, 2009;Mautz, 2012;Ozdenizci et al, 2015;Yasir et al, 2016).…”

Robust Self-Contained Pedestrian Navigation by Fusing the IMU and Compass Measurements via UFIR Filtering

“…For example, in [3,4], the radio frequency identification (RFID)-based technologies have been proposed provide object self-localization. Yang and Shao and Ma et al proposed in [5,6] an autonomous positioning system operating on WiFi, which is also able to achieve the indoor localization in indoor environment. However, it should be pointed out that although the RFID-and WiFibased methods mentioned above are able to provide the position information in indoor environment, the accuracy of such approaches is on meter-level.…”

Scanning method for indoor localization using the RSSI approach

“…The closest match between the measurements in the database and the existing measurement is done using deterministic or probabilistic approaches. Improvements in the localization accuracy of the traditional fingerprint technique are achieved by incorporating the weighted fusion procedure (Ma et al, 2015). A fingerprint method using WiFi based on the similarity between unequal length sequences is used by Huang et al (2016) for localizing a robot.…”