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.
Highlights
With the extensive development of mobile Internet spurred by the widespread usage of mobile devices and mobile communication technology, the demands on Indoor Positioning Service (IPS)increases unceasingly
Since the Global Positioning System [4] (GPS) technology mainly relies on signal propagation in the air, buildings and their complex architecture will interfere with signal propagation, and limit the usage of GPS in indoor environments
Where AVGik, DEVik, x AVGk, AVGk is the average value of the kth original WiFi signal strength received at the target place, AVGik is the average value of the kth original WiFi signal strength in the ith possible fingerprint, DEVik is the standard deviation of the kth original WiFi signal strength in the ith possible fingerprint
Summary
With the extensive development of mobile Internet spurred by the widespread usage of mobile devices and mobile communication technology, the demands on Indoor Positioning Service (IPS). The accuracy of the fingerprinting approach is far from adequate Up to this point, many new researches have been put forward to address the problem of the time and energy costs, and they have worked out really well. To improve positioning accuracy of traditional location fingerprinting algorithm, this paper proposes an improved WiFi indoor positioning algorithm by weighted fusion. The proposed algorithm based on traditional location fingerprinting algorithm consists of two stages: Offline acquisition process and online positioning process. The online positioning process first uses pre-match method to select the candidate fingerprints to shorten the time for positioning; it uses the improved Euclidean distance and the improved joint probability to calculate two intermediate results; and it obtains the final result from these intermediate results by weighted fusion.
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