A Case Study of Cross-Floor Localization System Using Hybrid Wireless Sensing

Abstract

The indoor positioning system based on Micro Electro Mechanical Systems (MEMS) sensors is featured with short-term high accuracy, whose current positioning performance depends on the historical positioning result. Therefore, MEMS positioning has long-time error accumulation. The fingerprint positioning of Bluetooth Low Energy (BLE) is independent of accumulative error, but there is an irregular jump error in the positioning result, which limits the positioning accuracy. Furthermore, the actual commercial positioning systems generally require the consecutive positioning in multi-floor environment. Based on this, this paper proposes a data fusion algorithm based on BLE and MEMS for indoor cross-floor positioning. Firstly, we denoise the fingerprint database by clustering, outlier detection, and filtering algorithms. Then, the extended Kalman filter is employed to complete the optimal estimation of the two-dimensional target position according to the robust M estimation. Finally, the barometer and geographical position information are used to achieve the height estimation of the target. This paper also carries out a large number of engineering verification. The experimental results show that the algorithm can suppress the cumulative error effectively caused by low-cost MEMS sensors, and solve the problem of irregular jump error caused by Received Signal Strength Indicator (RSSI) jitter. In the indoor multi-layer environment, the proposed system achieves the horizontal and vertical positioning Root Mean Square (RMS) errors less than 0.9 m and 0.35 m respectively. In addition, we have verified the stability of the designed system through the long-time test.