# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 3 Related Works
### Conventional NLOS Identification Techniques in UWB  
As already mentioned in Section 1, identification of NLOS and LOS in UWB communications is not new. There have been several proposals in the literature [9; 16; 27; 28; 31; 32; 33] to identify and mitigate the NLOS conditions in UWB. However, the identification process of the MP condition is usually  
Figure 2: Illustration of the LOS, NLOS, and MP conditions in UWB ranging systems: (**a–c**) Comparison of the FP power, the CIR power, and the difference between the two power levels (FP and CIR power) in three scenarios: (**a**) LOS, (**b**) NLOS, and (**c**) MP conditions. The measurement was conducted for the three scenarios for random movement between 1 m and 24 m distances. (**d**) Comparison of the measured distance errors in the three mentioned conditions (LOS, NLOS, and MP) in static scenarios. ignored in the literature, although the effects of the MP conditions in UWB ranging systems were acknowledged as important aspects in [3; 8; 13; 21]. Conventionally, the NLOS detection in UWB is always regarded as a binary classification problem. The traditional NLOS detection methods can be coarsely categorized as:  
* Identification of the NLOS situation based on a binary hypothesis test [27]
* NLOS detection based on the change of the signal-to-noise ratio (SNR) [28]
* NLOS identification based on channel impulse response [9; 31]
* NLOS detection techniques based on the multi-path channel statistics such as the kurtosis, the mean excess delay spread, and the root mean squared delay spread [16; 33]
* Detection of the NLOS condition using the received signal strength (RSS) [32; 33]  
In brief, the conventional NLOS identification approaches mainly rely on the statistical condition of the received signals in UWB communications. Figure 2a-c demonstrates these scenarios by comparing the first-path (FP) signal power and the channel impulse response (CIR) power for the three conditions (LOS, NLOS, and MP). Among the mentioned NLOS detection methods, the threshold approach presented by Decawave in [34] has been widely used in different UWB applications and system implementations [23; 34; 35]. This is accomplished by taking the difference between the estimated total received (RX) power and the first-path (FP) power using the following equations [34] (Figure 2a-c):  
\[FP\ Power\ Level=10\cdot log_{10}(\frac{F_{1}^{2}+F_{2}^{2}+F_{3}^{2}}{N^{2}})-A \tag{1}\]  
where \(F_{1}\), \(F_{2}\), and \(F_{3}\) are the first, second, and third harmonics of the first-path signal amplitudes for signal propagation through wireless media as in the multi-path, NLOS, and/or LOS scenarios [34; 35]. \(N\) is the value of the preamble accumulation count reported in the DW1000 chip from Decawave. \(A\) is a predefined constant value, which has 133.77 for a pulse repetition frequency (PRF) of 16 MHz and 121.74 for a PRF of 64 MHz. The estimated received power (RX) level can be defined as:  
\[RX\ Power\ Level=10\cdot log_{10}(\frac{C\cdot 2^{17}}{N^{2}})-A \tag{2}\]  
where \(C\) is the value of the channel impulse response power reported in the DW1000 chip. Therefore, the metric that specifies the conditions of LOS and NLOS in the threshold method can be achieved by computing the difference between the received and first-path power [34] as:  
\[Threshold\ Power=RX\ Power\ Level-FP\ Power\ Level \tag{3}\]  
In the conventional threshold approach, the measured distance is classified as a LOS when the threshold power using (3) is less than 6 dBm and defined as an NLOS when it is more than 10 dBm [34]. This is a sub-optimal acceptable solution as our particular experimental evaluation results show in Figure 2. That is, the mean value of the threshold power including its standard deviation in the LOS condition using (3) is 4.12 \(\pm\) 1.13 dBm (Figure 2a) and in the the NLOS condition is 10.75 \(\pm\) 5.51 dBm (Figure 2b). However, the solution is not optimal as much fluctuation can occur as described in the experimental measurement data (Figure 2a-c).