## 5 Discussion and Conclusions  
This work provides a comprehensive review of UWB and Wi-Fi RTT indoor positioning technologies, with particular attention on three aspects: positioning principles, NLOS signal identification and ranging error correction, and advanced positioning methodologies. The discussion encompasses the strengths and weaknesses of UWB and Wi-Fi RTT positioning, with a particular emphasis on analyzing NLOS errors, their impact, and correction techniques. Moreover, it sheds light on recent research trends and future directions, aiming to guide students, young researchers and engineers in exploring innovative ideas in this field. UWB and Wi-Fi RTT signals suffer from intricate propagation challenges, as environmental factors such as physical structures and materials influence signal propagation, especially in NLOS conditions characterized by reflections, diffractions, and the multipath effect. It is crucial to address these complexities for accurate ranging measurements. The variability in NLOS conditions causes another layer of complexity, making the development of universally applicable correction models a daunting task. Many existing correction methods may pose computational challenges, particularly with large datasets or real-time applications. Keeping a balance between accuracy and computational efficiency remains an ongoing endeavor. For instance, although machine learning approaches can extract valuable insights and classify data effectively, the data-processing efficiency needs to be optimized by determining the optimal training dataset size for specific machine learning algorithms, and it remains a task for future exploration. Due to the physical limitation of a single type of sensors, positioning systems with either UWB or Wi-Fi RTT signals suffer from challenges regarding accuracy, reliability, continuity, etc. Advanced integrated positioning through data fusion is essential for improving the positioning performance. The fusion of relative and absolute positioning information derived from diverse sensors provides synergistic advantages, and it will be the dominant solution for prospective development. The convergence and integration of different sensors may result in novel integration models, such as hybrid neural network architectures incorporated with Kalman filtering. In practical applications, both UWB and Wi-Fi RTT positioning systems face significant challenges in handling computational load and managing latency while keeping affordable system costs. UWB and Wi-Fi RTT positioning technologies typically require substantial processing power to handle the dense data from multiple sensors in real time. This can become a bottleneck, particularly in fast-paced environments such as automated guided vehicle (AGV) tracking in factories, where even a few milliseconds of delay can disrupt operations. The computational demands can exacerbate latency issues, impacting applications that require near-instantaneous location updates. To address these challenges, various strategies can be implemented to achieve low latency and high accuracy. For instance, edge computing can significantly reduce data transmission delays, while distributed computing architectures enable parallel processing across multiple nodes, enhancing efficiency. Additionally, optimizing positioning algorithms--such as simplifying Kalman filters or employing lightweight neural network models--can further lower latency and computational demands. Hardware acceleration is also crucial, with technologies like Field-Programmable Gate Array (FPGA), Graphics Processing Unit (GPU), or Application-Specific Integrated Circuit (ASIC) providing significant improvements in real-time processing performance. Moreover, optimizing wireless communication protocols, such as adopting low-latency Wi-Fi 6 or 5G networks, and dynamically adjusting data update frequencies based on system operational states allow for flexible resource allocation and improved overall performance.