# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 2 Problem Description  
The primary goal of the identification process in wireless communications is to detect the existence of an NLOS and/or MP condition in a communication between a transmitter and a receiver. This process is crucial because the multi-path effects and the NLOS conditions strongly influence the accuracy of the measured distances in wireless communications. As an example, Figure 2d compares the error of measured distances in static scenarios in the LOS, NLOS, and MP conditions in UWB based on our experimental data. Figure 2a-c also illustrates the comparison of the conventional identification techniques for the three mentioned classes (LOS, NLOS, and MP) based on the first-path (FP) power level and the channel impulse response (CIR) (a more detailed description is given in Section 3.1).  
The experimental evaluation results in Figure 2d suggest that the magnitude of the error in NLOS and MP conditions is considerably larger than the LOS condition compared to the ground truth reference. Moreover, the error introduced by the MP condition is significantly lower than the completely blocked NLOS condition, where the signal needs to penetrate the obstacle to reach the receiver. Indeed, this depends on the materials and other factors of the obstacles [3]. However, the result in Figure 2d, which is blocked by a human in this experiment, indicates that the NLOS condition introduces the highest impact on the measured distance errors. This motivated us to classify the UWB ranging systems into three classes (LOS, NLOS, MP) to improve the location accuracy in the UWB localization system. The classified ranging information is applicable in any positioning algorithm [6; 14] to mitigate the biases effectively [2; 18; 20] caused by the NLOS and MP conditions. It should be noted that the measured distances in Figure 2d were conducted in the static scenario at approximately a 6 m distance between the anchor and tag for the three classes. The ground truth references of the distance were measured using a laser distance meter, CEM iLDM-150 ([[http://www.cem-instruments.in/product.php?pname=iLDM-150](http://www.cem-instruments.in/product.php?pname=iLDM-150)]([http://www.cem-instruments.in/product.php?pname=iLDM-150](http://www.cem-instruments.in/product.php?pname=iLDM-150))), which provided an accuracy of \(\pm 1.5\,\mathrm{mm}\) according to the datasheet of the manufacturer. Regarding the ranging errors in UWB in a static scenario, a more rigorous evaluation was conducted in our previous work [8], where its corresponding experimental research data were given in [12]. The result of MP conditions in Figure 2d was based on a scenario when there was no obstacle between the transceivers, as illustrated in Figure 1 (Section 1). To be precise, the measurement was conducted in indoor environments where multiple reflections from walls occurred in a narrow corridor. One of the reasons the error occurred in the MP condition in this scenario was because of the preamble accumulation time delay (PATD) in the coherent receiver of the UWB chip [8]. PATD is affected by the presence of multi-path in wireless measurements. It is more notable when the arrivals of the reflected signal are within the chip period of the first path signal [8]. Moreover, it is worth mentioning that the error deviations in Figure 2d correspond to a single range (a tag to an anchor) in the measurement. In general, at least three ranges (usually more in multilateration methods) of such measures are necessary for UWB localization [6; 14]. This implies that the combination of such errors in a ranging phase contributes to a significant impact on the overall system performance.