Table 1.

Problems encountered in human detection in NLOS environments.

Limitations	Explanation-rationale
Limited real-world validation12–14	Many studies rely on simulations or controlled environments, which limit their validity in outdoor environments. In NLOS environments, which contain diverse environmental conditions, this weakens confidence in highly complex real-world environments
Sensor-specific focus15–17	Research has shown that they rely on single sensors (Radar, LiDAR, GNSS). This prevents and limits the ability to account for information revealing attributes from different sensors
Computational complexity14,18	The methods and artificial intelligence models used for NLOS detection are generally computationally expensive. This limits their adaptability to real-world conditions. This creates limitations in time-critical applications
Limited Dataset Variety6,19,20	The datasets used in detection studies using machine learning and artificial intelligence models are quite limited. These datasets have low generalizability to urban environments and are less robust in underground debris studies. One of the limitations is the lack of a dataset suitable for changing geographic and environmental conditions
Limited long-term stability21,22	Some sensors cause error accumulation in location determination methods in long-term, degraded environments. This degrades positioning accuracy and reduces reliability
Inadequate consideration of dynamic obstacles12,23	Studies focus on static and simplified scenarios. Studies have neglected the dynamic impact of mobility. This suggests a focus on standard scenarios and inadequate consideration of dynamic obstacles
Hardware and cost constraints16,24	Sensors used in NLOS environments typically utilize high-precision tools such as LiDAR and Radar. This suggests that hardware constraints and cost-limited applications are being adopted