# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 1 Introduction  
Indoor localization systems enable several potential applications in diverse fields. A few examples where positioning is crucial include tracking valuable assets and personal devices in IoT, ambient assisted living systems in smart homes and hospitals, logistics, autonomous driving systems, customer tracking systems in shopping and public areas, positioning systems in industrial environments, and mission-critical systems such as an application for firefighters and soldiers [1; 2; 3]. Among several technologies available for indoor localization described in the literature, ultra-wideband (UWB)technology [1; 4; 5] plays an increasingly important role in precise indoor localization systems due to its fine ranging resolution and obstacle-penetration capabilities [2; 3; 6]. In wireless ranging systems including UWB technology, the distance between the transmitter and receiver is estimated by measuring the time-of-flight (TOF) between the two transceivers and multiplying it by the speed of light [7; 8]. However, the ranging algorithm assumes that the _TOF_signal is always in a direct line-of-sight (LOS) condition. Therefore, non-line-of-sight (NLOS) [9; 10; 11] and multi-path (MP) [3] conditions cause a positive bias in the estimated distances. Figure 1 expresses an abstract view of the LOS, NLOS, and MP conditions in typical wireless communications. The figure shows how a signal sent from a tag device (green pyramid shape in the middle) can be received in different scenarios at the anchor nodes (yellow pyramid shapes). In Figure 1, we define two possible MP conditions in wireless communication. The first condition is clear because the first path signal is completely blocked by the obstacle, and the only received signal in the measurement is based on the bounded signal from the transmitter. However, distance measurement in wireless communication could also be distorted by multiple reflections of signals even if there is no direct obstacle between the transceivers. For instance, wireless measurement is conducted in a narrow corridor, tunnel, etc. We confirmed the error caused by such MP conditions using UWB in our previous work [8]. The research data of the mentioned work are publicly available in [12]. Similar results based on UWB were also reported in [13]. Therefore, differentiation between the LOS, NLOS, and MP conditions in wireless ranging systems is important for precise localization systems. This paper discusses the vital role of classifying the LOS, NLOS, and MP scenarios in UWB ranging system using machine learning (ML) approaches. By understanding the defined three classes, a positioning algorithm [6; 14] can mitigate the biases caused by NLOS and MP conditions, i.e., by giving different weights to each class. We proposed such a mitigation technique in our previous work [2]. The common identification and mitigation techniques for the NLOS condition in UWB can be found in [9; 15; 16] and the references provided therein. In fact, the multi-class identification of UWB measurement data (LOS, NLOS, and MP) in the real world is challenging in indoor environments because a variety of physical effects can distort the direct path LOS signal in different ways [3; 17] (Figure 1). This includes walls, furniture, humans, the orientation of the UWB antenna, etc. Therefore, machine learning methods are attractive solutions for solving such a problem. Identification and mitigation techniques of the NLOS condition in UWB, or wireless communications in general, using ML methods are not new. It has received significant interest  
Figure 1: Illustration of LOS, NLOS and multi-path (MP) scenarios in a UWB-based ranging system. recently years [9; 10; 11; 17; 18; 19; 20; 21; 22; 23]. However, the major contributions in the literature address the binary classification between the LOS and NLOS in the UWB ranging system.