Indoor positioning technology has been widely used in today’s life, but due to the influence of multipath effect, the positioning signal is attenuated or even interrupted seriously, resulting in obvious reduction or even failure of positioning accuracy. Therefore, the emerging multi-sensor joint positioning has become the general trend of the development of positioning technology, in which Ultra-Wide Band (UWB) and Inertial Measurement Unit (IMU) have their own features in positioning and navigation. So this paper combines the advantages of UWB and IMU to achieve accurate positioning in complex environment. Firstly, the signal transmission law in complex environment is obtained by distinguishing Line of Sight (LOS) from NLOS (Non Line of Sight) environment. Secondly, the maximum likelihood estimation algorithm is used to eliminate the influence of NLOS on the transmitted signal, and then the extended Kalman filter information fusion strategy is used. The ranging information of UWB and the angle information of IMU are fused to realize the accurate positioning of UWB in complex environment. Finally, the experimental results show that the performance of the joint positioning proposed in this paper is obviously better than that of a single sensor compared with single UWB and single IMU positioning. It provides more solutions for accurate indoor positioning of multi-sensor fusion.
The existing positioning methods that use received signal strength indication (RSSI) and channel state information (CSI) may suffer from multipath and shadowing in a complex wireless environment, which can result in more positioning errors. This paper proposes a method for accurate multilabel positioning in the non-line-of-sight (NLOS) environment. First, the position is roughly estimated using the orthogonal variable spreading factor (OVSF-TH) algorithm, which can automatically match the signal interference. The ultra-wideband (UWB) spectral density and pulse amplitude in the time domain are used to determine the direction of the label and enhance estimation of the mobile label direction. Then, the location of the tag is obtained by triangulation, and a coordinate-based coordinate estimation method is proposed to calculate the relative displacement of multiple tags to determine the label position. Finally, by setting up a real experimental environment, the influence of the number of base stations on the accuracy and the performance of the localization method under different circumstances are analyzed. The theoretical analysis and experimental results show that the method is simple to deploy, inexpensive, and very accurate in terms of positioning, having a clearly effective indoor positioning accuracy. Compared with other existing positioning methods, this method can achieve more accurate positioning. Moreover, it has important theoretical and practical applicability because of the reliability and accuracy of indoor positioning in an NLOS environment.
In a precision positioning system, one important source of positioning error is the clock synchronization problem, which is caused by multiple base stations. Therefore, eliminating the clock synchronization problem in a no-line-of-sight (NLOS) environment plays an important role in reducing errors in a positioning system. To address this problem, this study designs a practical experimental environment and proposes the concepts of Time Difference of Arrival (TDOA) and non-line-of-sight-Time Difference of Arrival (N-TDOA). First, the improved TDOA algorithm is used to determine the tag’s position; second, the tag’s trajectory is drawn at different times; then, a map of the monitored area is loaded, and the tag trajectory is displayed in the actual experimental environment. The experimental results show that the N-TDOA algorithm synchronizes the base stations at the algorithmic layer; thus, deploying network cables or wires to achieve clock synchronization is unnecessary: wireless deployment can be used. In an NLOS environment, the N-TDOA method significantly improves the positioning accuracy compared with that of other algorithms, which facilitates further trajectory tracking research. Overall, the proposed approach improves both the accuracy and stability of trajectory tracking.
In a precise positioning system, weak signal errors caused by the influence of a human body on signal transmission in complex environments are a main cause of the reduced reliability of communication and positioning accuracy. Therefore, eliminating the influence of interference from human crawling waves on signal transmissions in complex environments is an important task in improving positioning systems. To conclude, an experimental environment is designed in this paper and a method using the Ultra-Wideband (UWB) Local Positioning System II (UWB LPS), called Bayesian Compressed Sensing-Crawling Waves (BCS-CW), is proposed to eliminate the impact of crawling waves using Bayesian compressive sensing. First, analyse the transmission law for crawling waves on the human body. Second, Bayesian compressive sensing is used to recover the UWB crawling wave signal. Then, the algorithm is combined with the maximum likelihood estimation and iterative approximation algorithms to determine the label position. Finally, through experimental verification, the positioning accuracy of this method is shown to be greatly improved compared to that of other algorithms.
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