With the increasing demand for location-based services (LBS) in daily production and life, indoor positioning has gradually become one of the current research hotspots. The most pressing challenge in indoor positioning is achieving high-precision location in complex indoor environments. Aiming at the problem that severe non-line-of-sight (NLOS) interference with UWB positioning accuracy in complex indoor environments, this article proposes a novel indoor UWB NLOS correction positioning method based on anchor LOS/NLOS map, which utilizes prior environmental information. After anchors are deployed, the LOS/NLOS map is generated using the prior environmental information. Then the line-of-sight (LOS) anchors are selected for position solution, and the observations under the influence of the NLOS anchors are corrected. Then the trajectory is optimized using a Map-Modified Kalman filtering algorithm, and it adopts a method to solve the position of two LOS anchors by using trajectory constraints, which can deal with more complex indoor environment. The experimental results show that the NLOS correction method in this article has better accuracy than the traditional filtering algorithm, especially in NLOS environment with few anchors. Compared with the Standard Kalman filtering algorithm, the average positioning accuracy is improved by 69.30%, and compared with the robust Kalman filtering algorithm, the average positioning accuracy is improved by 33.77%.

Ultra-wideband (UWB) time-of-flight (TOF)-based ranging information in a non-line-of-sight (NLOS) environment can display significant forward errors, which directly affect positioning performance. NLOS has been a major factor limiting the improvement of UWB positioning accuracy and its application in complex scenarios. Therefore, in order to weaken the influence of the indoor complex environment on the NLOS environment of UWB and to further improve the performance of positioning, in this paper, we first analyze the factors and characteristics of NLOS formation in an indoor environment. The NLOS is divided into fixed NLOS influenced by spatial structure and dynamic random NLOS influenced by human occlusion. Then, the anchor LOS/NLOS information map is established by making full use of indoor spatial a priori information. On this basis, a robust adaptive extended Kalman filtering algorithm based on the anchor LOS/NLOS information map is designed, which is not only effectively able to exclude the influence of spatial NLOS, but can also optimize the random error. The proposed algorithm was validated in different experimental scenarios. The experimental results show that the positioning accuracy is better than 0.32 m in complex indoor NLOS environments.

Location information is the core data in IoT applications, which is the essential foundation for scene interpretation and interconnection of everything, and thus high-precision positioning is becoming an immediate need. However, the non-line-of-sight (NLOS) effect of indoor complex environment on UWB signal occlusion has been a major factor limiting the improvement in ultra-wideband (UWB) positioning accuracy, and the optimization of NLOS error has not yet been studied in a targeted manner. To this end, this paper deeply analyzes indoor scenes, divides NLOS into two forms of spatial occlusion and human occlusion, and proposes a particle filtering algorithm based on LOS/NLOS mapping and NLOS error optimization. This algorithm is targeted to optimize the influence of two different forms of NLOS, using spatial a priori information to accurately judge the LOS/NLOS situation of the anchor, optimizing the NLOS anchor ranging using IMU to project the virtual position, judging whether the LOS anchor is affected by human occlusion, and correcting the affected LOS anchor using the established human occlusion error model. Through experimental verification, the algorithm can effectively suppress two different NLOS errors of spatial structure and human occlusion and can achieve continuous and reliable high-precision positioning and tracking in complex indoor environments.

Indoors, the ultrawideband (UWB) signal line-of-sight (LOS) propagation is affected by the complexity of space structure, and the UWB time-of-flight (TOF) based ranging information in the non-line-of-sight (NLOS) environment will show evident positive errors. Therefore, the complexity of indoor spatial structure has been the main reason limiting the improvement of UWB positioning accuracy, yet the error characteristics caused by NLOS diffraction propagation have not been extensively studied. In this paper, firstly, the diffraction propagation characteristics of UWB in NLOS environment and its influencing factors are analyzed experimentally, based on which the diffraction propagation characteristics are studied and the relationship between diffraction delay and diffraction angle is explored. Finally, the time-delay model of UWB diffraction propagation is designed and the validity of the model is verified, and the range accuracy is improved by 82.61% after compensation.

In geodetic surveying, input data from two coordinates are needed to compute rigid transformations. A common solution is a least-squares algorithm based on a Gauss–Markov model, called iterative closest point (ICP). However, the error in the ICP algorithm only exists in target coordinates, and the algorithm does not consider the source model’s error. A total least-squares (TLS) algorithm based on an errors-in-variables (EIV) model is proposed to solve this problem. Previous total least-squares ICP algorithms used a Euler angle parameterization method, which is easily affected by a gimbal lock problem. Lie algebra is more suitable than the Euler angle for interpolation during an iterative optimization process. In this paper, Lie algebra is used to parameterize the rotation matrix, and we re-derive the TLS algorithm based on a GHM (Gauss–Helmert model) using Lie algebra. We present two TLS-ICP models based on Lie algebra. Our method is more robust than previous TLS algorithms, and it suits all kinds of transformation matrices.

In the complex indoor environment of ships, personnel position is difficult to know in real time. In this paper, an indoor positioning management system in large ship based on virtual reality and ultra-wide-band(UWB)/Inertial Measurement Unit(IMU) fusion algorithm has been studied. Unity 3d is used to build a virtual reality platform which receive UWB and IMU data in real time. The coordinates of people in ship are calculated based on Kalman filtering algorithm. The virtual reality system is used to identify non-line of sight (NLOS) area and to reduce error caused by NLOS environment. Personnel position coordinates and the trajectory are displayed in the virtual platform. The experimental results show that the average positioning error of the fusion algorithm is 0. 08m, which is about 40% higher than the positioning accuracy of the single positioning method of UWB.