## 4 Advanced Positioning Methodologies Using UWB and Wi-Fi RTT Signals
### Integrated Positioning of Data Fusion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **Sensors** & **Fusion** & **Algorithm** & **Platform** & **Experiment** & **Accuracy (m)** & **Limitations** \\ \hline
2018 [82] & Monocular camera, UWB & Tight coupling & Joint nonlinear optimization on Lie-Manifold & Vehicle & The EuRoC MAV datasets & 0.036 & The cost of generating global maps is high \\ \hline
2021 [83] & IMU, UWB & Loose coupling & Deep learning-based speed estimation; KF-based position integration & Smartphone & An area of 5 m \(\times\) 31 m & 0.21 m & The design trajectory is simple \\ \hline
2021 [84] & IMU, UWB & Tight coupling & A complex Kalman filter for integration & Smartphone & A closed area of 9 m \(\times\) 18 m & / & No NLOS impact \\ \hline
2021 [85] & Camera, IMU, UWB & Tight coupling & Mutual-information-based residual optimization & Robot & An indoor environment with the dimension about 2.70 m \(\times\) 1.80 m using a mobile robot & 0.16 & The environment is relatively simple with fixed UWB anchors \\ \hline
2021 [86] & mono-VIO, UWB & Tight coupling & Residual-based optimization & UAV & Simulates the UAV flights using Gazebo and RottoS & \textless{}0.2 & Pursuing real-world experiments \\ \hline
2021 [87] & mono-VIO, UWB & Tight coupling & Residual-based optimization & Robot & “Loop” tests tin a 6 m \(\times\) 6 m & Only-one-robot scenarios, and \(\textless{}0\)m \(\times\) 10 m & 0.06–0.14 & multi-robots scenarios need to be extended \\ \hline
2021 [88] & VIO, UWB & Tight coupling & Graph optimization & Robot & An indoor environment with the dimensions of about \(\times\)0.2 & The method considers an ideal environment considering only a situation where the UWB signal is a LOS mobile robot & The method considers an ideal environment considering only a situation where the UWB signal is a LOS \\ \hline
2023 [88] & UWB, Measured-inertial odometry & Loose coupling & Distributed pose graph optimization & UAV with dimensions of 5 m \(\times\) 4 m \(\times\) 2.5 m using two UAVs & 0.2 & The method for communication is high \\ \hline
2024 [90] & IMU, UWB & Loose coupling & An Error-State-Kalman-Filter-based real-time fusion & Electric vehicle & A tunnel of 140 m \(\times\) 5 m \(\times\) 4 m \(\times\) 5 m \(\times\) 4 m & \begin{tabular}{c} \textless{}0.4 m in LOS \\ cases with a moving speed \\ \end{tabular} & \
\begin{tabular}{c} \textless{}0.02 in LOS \\ cases with a \\ NLOS cases \\ of 36 km/h \\ \end{tabular} &
\begin{tabular}{c} No experiment in the \\ NLOS cases \\ \end{tabular} \\ \hline
2024 [91] & Vision SLAM, UWB & Tight coupling &
\begin{tabular}{c} EKF with \\ threshold \\ detection and \\ adaptive \\ measurement \\ noise estimator \\ \end{tabular} & Robot & Obstacle-free and obstacle-rich environments & 0.076–0.082 & 0.076–0.082 & 0.076–0.082 & The experiments are conducted in a small field, leading to fewer accumulated errors \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of UWB-based integrated positioning studies. Among these studies, the mainstream positioning fusion methods are Kalman filtering and factor graph optimization. Kalman filtering is a recursive method based on current observation data and previous state estimation, which can efficiently realize real-time state estimation. EKF and unscented Kalman filtering (UKF) are commonly used nonlinear filtering methods in fusion positioning systems. EKF approximates by linearizing the Jacobian matrix, while UKF uses unscented transformation to improve accuracy under strong nonlinear conditions. Adaptive Kalman filtering can dynamically adjust noise parameters to enhance robustness, but the computational resource requirements are high. However, Kalman filtering has strict assumptions on the noise model and usually requires the error distribution to be Gaussian, which makes it less robust in abnormal situations such as NLOS and multipath effects. At the same time, when dealing with nonlinear systems, the linearization of EKF may introduce errors, and although UKF has improvements, it increases the computational complexity.