## 3 NLOS Ranging Error and Correction Methods of UWB and Wi-Fi RTT Signals
### NLOS Ranging Error Mitigation
The authors of [67] summarized three machine learning methods, including random forest, least squares support vector machine, and deep neural network, which are effective for real-time NLOS/LOS identification leveraging Wi-Fi RSSI and RTT ranging measurements. Recent advancements include the utilization of a neural network for processing raw measurements of Wi-Fi RTT [68]. Ref. [69] reported a convolutional neural network (CNN) using CIR with NLOS mitigation, which achieved a mean error of 65 cm with three anchors under NLOS conditions. An adaptive NLOS mitigation method was reported in [70], which used and optimized machine learning models, including deep neural network (DNN), CNN and long short-term memory (LSTM), and achieved a mean error of 26.1 cm in complex NLOS indoor environments. Effectivelyaddressing these NLOS challenges is imperative for improving the accuracy and reliability of indoor positioning systems based on UWB and Wi-Fi RTT technologies. Regardless of biased raw distance measurements or varying measurement patterns across multiple indoor sites or between LOS and NLOS conditions at the same site, the neural network adapts to these patterns flexibly and effectively enhances the ranging accuracy. These methods adapt effectively to complex environments and scenarios. However, the machine learning approach requires a large volume of labeled data and computational resources, which are the main challenges to be addressed when applying these methods. Fingerprinting: Fingerprinting positioning is an effective approach for indoor positioning. It first maps the environment of interest and constructs the radio map or what is named signal fingerprint, which reflects the spatial distribution of radio signals. The effects of NLOS and multipath signals have been represented implicitly. When the radio map is used for location resolution in the subsequent online positioning stage, the positioning observables include NLOS components similar to those in the radio map, which are mainly related to the environment and are assumed to remain statistically correlated at both stages of radio map construction and online positioning. Hence, NLOS conditions provide useful information for the fingerprinting approach. In [71], trilateration and fingerprinting were integrated to improve the positioning performance in NLOS conditions, and [72] reported an UWB indoor positioning method based on LOS/NLOS signal mapping, which utilized prior environmental maps to tackle severe NLOS interference in intricate indoor settings. In general, the fingerprinting approach yields relatively high localization accuracy under NLOS conditions, whereas it may be less effective in near-LOS scenarios unless the radio map includes sufficiently dense fingerprints, which, however, incurs a large offline labor cost. Optimization: This approach employs optimization algorithms to mitigate the impact of NLOS signals, such as convex optimization, particle swarm optimization, genetic algorithms, etc., and the optimization outcome is utilized to assess the presence of NLOS signals and conduct corresponding correction. For instance, [73] reported the method of tightly integrating IMU and multiple UWB data using the graph optimization model. This kind of method excels in fully utilizing information from CIR data; however, these methods entail high computational complexity and may encounter the local optima problem [74, 75]. It is particularly challenging to deal with multipath effect due to their intricate propagation modes, leading to varying ranging results even across different channels within the same environment. To address this issue, a non-convex optimization method considers the spatial constraints of virtual positioning clients to effectively solve the optimal estimation problem [76].