Abstract
Aiming at the problems of high time overhead, low positioning accuracy, and inability to meet the requirements of indoor positioning applications in WiFi and Pedestrian Dead Rockoning (PDR) positioning technologies, a cross-layer positioning method based on multi-sensor and WiFi fingerprint fusion is proposed. Firstly, Multi-dimensional scaling technology (MDS) is used in WiFi location, which reduces the overhead of offline stage of large area WiFi fingerprint location and improves the responsiveness of online location. Without limiting the holding mode, the high and low threshold detection method is used to reduce the error in gait detection of the PDR method. Unscented Kalman Filter (UKF) is used to fusion WiFi and PDR positioning results, output the optimal two-dimensional estimation, and finally use WiFi signal and multi-sensor information to determine the height of pedestrian position and output three-dimensional coordinates. The experiment results show that the proposed method have higher accuracy and better stability compared with WiFi and PDR positioning method.
Highlights
The market demand related to indoor localization services is expanding
The steps of the Multi-dimensional scaling technology (MDS) location algorithm are as follows: Step1: The distance matrix is constructed by using the Received Signal Strength Indicator (RSSI) vector distance between the M reference points, the matrix is recorded as Pm×m, and calculate the RSSI vector Euclidean distance between the M RSS reference points and the to-be-located point respectively, and build the distance matrix P(m+1)×(m+1) on the basis of the matrix Pm×m; Step2: The generalized Euclidean distance matrix P(m+1)×(m+1) is input into MDS algorithm and dimensionality reduction is carried out on P(m+1)×(m+1)
The average estimation error of the height estimation method proposed in this paper is 0.214m
Summary
The market demand related to indoor localization services is expanding. In modern cities, people spend about 80% of their life in indoor environments. Faced with the above problems, in this paper, a cross-floor location method based on multi-sensor and WiFi fingerprint fusion is proposed to solve the problems such as the time consuming of database construction in the offline stage of collaborative positioning systems, 3D location. In reference [32], based on the five ways of pedestrians holding mobile phones, the author combines PDR method, WiFi fingerprint technology and landmark method to improve the accuracy of real-time user location. In reference [38], inertial sensors and GPS built-in smart phones are used to realize indoor positioning, segmented signal frames are used to learn pedestrian speed, and pedestrian time series signals are classified, regressed, and predicted by multi-scale convolution and recurrent neural network model, which greatly reduces the construction cost of location fingerprint and achieves high positioning accuracy.
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