The existing methods for handling the NLOS errors mainly include : (1) realizing integrated UWB positioning by adding hardware other than UWB, (2) identifying and mitigating the NLOS errors by virtue of filtering algorithms, (3) mitigating the NLOS errors with the positioning algorithm based on convex optimization and (4) identifying and mitigating the NLOS errors based on the channel impulse response (CIR) signal characteristics. In this context, the recognition and mitigation of NLOS errors have become hot topics in the research on UWB positioning. Given the complexity of the actual positioning environment, non-line-of-sight (NLOS) errors, multi-path errors, clock drift errors and errors caused by antenna delay tend to occur in the UWB positioning process, among which the NLOS errors are the primary type, having a significant influence on positioning accuracy. In addition, since the UWB frequency band and the 5G frequency band are partially shared, the UWB signal will severely impact the demodulation of the 5G signal. However, 5G indoor positioning technology is still immature. The emergence of the fifth-generation mobile communication technology (5G) provides a new idea for high-precision indoor positioning.
#Ultra fractal 5 normalized iteration count algorithm Bluetooth
Compared with Wi-Fi, radio frequency identification, ultrasound, Bluetooth and other positioning technologies, ultra-wideband (UWB)-based positioning technology has many advantages, including centimeter-level positioning accuracy, good multi-path resistance, preferable resistance against the interference of other electronic signals from complex environments and strong penetrability, which not only endow it with high reliability but also facilitate the collection of dynamic data and real-time positioning of moving objects in complex environments. Under the influences of such factors as the blockage of buildings and the complexity of environments, the traditional outdoor global positioning system (GPS) satellite positioning technology is becoming unable to meet the requirements of indoor and outdoor positioning due to great positioning errors. The positioning accuracy was improved significantly. Furthermore, compared with those of the positioning algorithm based on fuzzy inference, the RMSEs in overall positioning were lowered by 12.89%. In the dynamic positioning experiment, compared with the adaptive anti-NLOS KF positioning algorithm, the RMSE was reduced by 43.31% in the overall positioning. In the static positioning experiment, the probability of producing an error range of less than 19.1 cm with the positioning algorithm combining fuzzy inference with adaptive anti-NLOS KF was 0.93, which was much better than the positioning algorithm based on fuzzy inference and the adaptive anti-NLOS KF positioning algorithm. At last, the range estimation information after error mitigation was taken as the ranging information of the LS positioning algorithm for target localization. Next, an adaptive anti-NLOS KF algorithm was developed to perform a second mitigation on the ranging errors after mitigation of the NLOS errors with the fuzzy inference, thereby further raising the range estimation accuracy. First of all, the NLOS errors of the channel impulse response (CIR) signal characteristics were estimated by the fuzzy inference algorithm and then initially mitigated. To reduce the influence of non-line-of-sight (NLOS) errors in the ultra-wideband (UWB) positioning process, a UWB positioning algorithm based on fuzzy inference and adaptive anti-NLOS Kalman filtering (KF) was proposed in this paper.
0 kommentar(er)
