The fusion of ultra-wideband (UWB) and inertial measurement unit (IMU) is an effective solution to overcome the challenges of UWB in nonline-of-sight (NLOS) conditions and error accumulation of inertial positioning in indoor environments. However, existing systems are based on foot-mounted or body-worn IMUs, which limit the application of the system to specific practical scenarios. In this paper, we propose the fusion of UWB and pedestrian dead reckoning (PDR) using smartphone IMU, which has the potential to provide a universal solution to indoor positioning. The PDR algorithm is based on low-pass filtering of acceleration data and time thresholding to estimate the step length. According to different movement patterns of pedestrians, such as walking and running, several step models are comparatively analyzed to determine the appropriate model and related parameters of the step length. For the PDR direction calculation, the Madgwick algorithm is adopted to improve the calculation accuracy of the heading algorithm. The proposed UWB/PDR fusion algorithm is based on the extended Kalman filter (EKF), in which the Mahalanobis distance from the observation to the prior distribution is used to suppress the influence of abnormal UWB data on the positioning results. Experimental results show that the algorithm is robust to the intermittent noise, continuous noise, signal interruption, and other abnormalities of the UWB data.

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This study presents a novel phase current reconstruction strategy for switched reluctance machines (SRMs) using two cross-winding current sensors. The phase currents are reconstructed by solving the linear equations associated with two adjacent phase currents in the different turn-on regions. The effect of current sensor offset and power transistor fault on the proposed reconstruction method is analysed. On the basis of the current difference at the rising edge of each drive signal, an offset sensor identification method is presented and two online compensation schemes are adopted. For power transistor short-circuit fault, the logic-judgment-based and freewheeling-time-based diagnostic methods are investigated and a virtual current sensor is introduced to ensure the effectiveness of the reconstruction process. The proposed phase current reconstruction strategy is free from power transistor open-circuit fault. In addition, the current reconstruction method is easily extended to SRMs with higher number of phases without additional current sensors. Simulations and experiments validate the effectiveness and flexibility of the proposed reconstruction strategy.

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As UWB high-precision positioning in NLOS environment has become one of the hot topics in the research of indoor positioning, this paper firstly presents a method for the smoothing of original range data based on the Kalman filter by the analysis of the range error caused by UWB signals in LOS and NLOS environment. en, it studies a UWB and foot-mounted IMU fusion positioning method with the integration of particle filter with extended Kalman filter. is method adopts EKF algorithm in the kinematic equation of particle filters algorithm to calculate the position of each particle, which is like the way of running N (number of particles) extended Kalman filters, and overcomes the disadvantages of the inconformity between kinematic equation and observation equation as well as the problem of sample degeneration under the nonlinear condition of the standard particle filters algorithm. e comparison with the foot-mounted IMU positioning algorithm, the optimization-based UWB positioning algorithm, the particle filter-based UWB positioning algorithm, and the particle filter-based IMU/UWB fusion positioning algorithm shows that our algorithm works very well in LOS and NLOS environment. Especially in an NLOS environment, our algorithm can better use the foot-mounted IMU positioning trajectory maintained by every particle to weaken the influence of range error caused by signal blockage. It outperforms the other four algorithms described as above in terms of the average and maximum positioning error.