## 2 Related Work
[29] studied an algorithm based on RSS residual weighting (RRW) to alleviate the line of sight nonlinearity and reduce positioning errors. In addition, some auxiliary sensors, such as IMUs, lidar, and R-GBD cameras, can be used in UWB systems to improve positioning accuracy. [PERSON] et al. [30] proposed an adaptive complementary Kalman filter method to perform fusion filtering on UWB and IMU data and track errors in variables, such as position, speed, and direction, which not only eliminates the ME but also corrects the deviation of velocity and acceleration caused by IMU drift. [PERSON] et al. [31] proposed a pedestrian indoor positioning method based on a UWB and a visual fusion algorithm. By combining the two sensors, the scale ambiguity of monocular circular simultaneous localization and mapping (SLAM) was solved, and the UWB positioning accuracy was improved by relocating the failed visual trajectory through the UWB system. [PERSON] et al. [32] combined UWB and two-dimensional LIDAR sensors to provide a more accurate and comprehensive image of the surrounding environment. Alternatively, UWB ranging can eliminate the accumulated error based on a LIDAR-SLAM algorithm. [PERSON] et al. [33] used a UWB- and IMU-integrated navigation system based on adaptive motion constraints to solve the short interruption problem of UWB systems. [PERSON] et al. [34] combined the UWB with ultra-high time resolution, good anti-multipath ability, and strong penetration ability, as well as the active composition and positioning ability of LIDAR, to improve positioning accuracy. [PERSON] et al. [35] used a tightly coupled GPS-UWB-IMU navigation system based on an improved robust Kalman filter for better positioning accuracy. [PERSON] et al. [36] introduced an antimagnetic ring to eliminate outliers of a UWB system in an NLOS environment and integrated inertial navigation system (INS) attitude information. [PERSON] et al. [37] studied an tight-coupling IMU-UWB accurate positioning system to obtain accurate position and orientation estimates. In summary, positioning accuracy is the most important performance parameter used in indoor wireless positioning systems, compared with precision, complexity, scalability, robustness, and cost. The mean distance error is adopted as the performance metric, which is the average Euclidean distance between the estimated location and the true location. However, location precision addresses how consistently the system works. The cumulative probability functions of the distance error can be used to measure the precision of a UWB-based positioning system [38]. Therefore, this study focuses on a dynamic error propagation model for accuracy performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. ** & **Authors/Year** & **Model and Algorithm** & **Quantization** & **Application Scenarios** & **Data Sources** \\ \hline
1 & [PERSON], 2003 & (a) Cross-correlation-based TODA & & Weighting functions & An unfilled 31 ns wide, 20 m deep, and 12 m high-ababetic chamber. & Simulated \\  & [16] & (b) TODA estimates from multiple antenna pairs. & & Weighting functions & An unfilled 13 m wide, 20 m deep, and 12 m high-abetic chamber. & Simulated \\  & [7] & (a) Nodes determine their location with the help of & Average mean and & A simulation software: Clom-Mosim, & Simulated \\
2 & [7] & (b) ETCB images algorithm. & & Range error and average & Low-rate UWB communication & Numerical Simulation \\
3 & [8] & (c) Fang iterative locating mobile node algorithm. & Positioning error & systems in the mobile environment. & Physical \\  & [7] & (a) BU control with UNW position & Orientation and position estimation & Clock deviation occurs in UWB. & Experiment \\
5 & \(Z\) [PERSON], 2018 & (a) An adaptive constraint used in the navigation filter. & 10-30F (10-35 cm) improvement for & Unexpolded ordinance & Experiment \\  & [3] & (b) MU-decing neural networks to determine the current dynamic state. & 5-10 s long outages. & mapping platform.