An integrity monitoring algorithm for WiFi/PDR/smartphone-integrated indoor positioning system based on unscented Kalman filter

Abstract

Indoor positioning navigation technologies have developed rapidly, but little effort has been expended on integrity monitoring in Pedestrian Dead Reckoning (PDR) and WiFi indoor positioning navigation systems. PDR accuracy will drift over time. Meanwhile, WiFi positioning accuracy decreases in complex indoor environments due to severe multipath propagation and interference with signals when people move about. In our research, we aimed to improve positioning quality with an integrity monitoring algorithm for a WiFi/PDR-integrated indoor positioning system based on the unscented Kalman filter (UKF). The integrity monitoring is divided into three phases. A test statistic based on the innovation of UKF determines whether the positioning system is abnormal. Once a positioning system abnormality is detected, a robust UKF (RUKF) is triggered to achieve higher positioning accuracy. Again, the innovation of RUKF is used to judge the outliers in observations and identify positioning system faults. In the last integrity monitoring phase, users will be alerted in time to reduce the risk from positioning fault. We conducted a simulation to analyze the computational complexity of integrity monitoring. The results showed that it did not substantially increase the overall computational complexity when the number of dimensions in the state vector and observation vector in the system is small (< 20). In practice, the number of dimensions of state vector and observation vector in an indoor positioning system rarely exceeds 20. The proposed integrity monitoring algorithm was tested in two field experiments, showing that the proposed algorithm is quite robust, yielding higher positioning accuracy than the traditional method, using only UKF.

Indoor Spatiotemporal Contact Analytics Using Landmark-Aided Pedestrian Dead Reckoning on Smartphones

“…Using the smartphone’s accelerometer to determine whether the pedestrian is stationary, or walking is straightforward as it directly reflects the moving acceleration. The magnitude of acceleration on three dimensions instead of the vertical part is employed as the input for peak findings to improve the accuracy, which can be expressed as: where denote the three-axis accelerometer values in the smartphone [ 42 ]. A peak is detected when is greater than the given threshold.…”

“…Heading information is a critical component for the entire PDR implementation, which seriously affects localization accuracy. To avoid the accumulative error in the direction estimation based on the gyroscope, and short-term direction disturbances based on the magnetometer, the combination of the gyroscope and magnetometer is typically adopted for heading estimation [ 42 ]. The current magnetometer heading signals, current gyroscope readings, and previously fused headings are weight-averaged to form the fused heading.…”

Applying a ToF/IMU-Based Multi-Sensor Fusion Architecture in Pedestrian Indoor Navigation Methods

“…After presenting the Openshoe method [ 4 ] based on the Pedestrian Dead Reckoning (PDR) system with the foot-mounted IMU, and by emerging the Zero-Velocity Potential Update (ZUPT) method in [ 5 ], numerous Kalman Filter (KF) and GPS-based indoor localization methods have been presented. For example, online calibration of INS/ZUPT using Extended Kalman Filter (EKF) [ 6 ], Constrained Square-Root Unscented Kalman Filter (CSR-UKF) and UKF methods [ 7 , 8 ], magnetic field Gradient-based EKFs [ 9 ] are evaluated and discussed. Moreover, various tightly and loosely coupled integrations of indoor PDR systems are designed and evaluated using Bluetooth [ 10 ], GPS [ 11 ], and Radio Frequency Identification (RFID) [ 12 ], which showed a more accurate performance compared to the stand-alone INS and Openshoe methods.…”