# PEiD: Precise and Real-Time LOS/NLOS Path Identification Based on Peak Energy Index Distribution
## 2 Related Work  
Prior research in the field focuses on exploring the random nature of NLOS paths through a range of specific parameters measured from the received signal. These parameters include range [20], ToA [22; 23], Received Signal Strength (RSS) [21], and AoA [19]. As unstable channel properties of NLOS can severely disturb the measurement of the parameter, researchers can determine the NLOS paths by capturing abnormal values. For instance, [PERSON] et al. [20] model the NLOS error as a deterministic additive term in addition to a white zero-mean Gaussian noise. Autocorrelation and eigenvector analyses are adopted to find the error. [PERSON] et al. [19] investigate AoA measurements of Wireless Sensor Networks (WSN) and adopt the theorem of Neyman-Pearson to detect the occurrence of abnormal values. To reduce the errors caused by NLOS paths, [PERSON] et al. [22] propose an NLOS identification and mitigation method based on a Kalman Filter (KF). For more accurate NLOS identification, other researchers also exploit techniques of machine learning such as Support Vector Machines (SVM) [23] and neural networks [21]. The idea of statistics-based solutions is simple and effective. However, their methods may not be applicable to WiFi sensing. First, some of the works mentioned above are based on cellular networks and WSNs. Their application scenarios can be different from that of WiFi in indoor environments. Second, due to the limitation on bandwidth, most commodity WiFi devices are not able to provide reliable measurements of ToA or AoA. That increases the difficulty of detecting NLOS errors. Distinct from the approaches based on statistical parameters, UWB-based solutions focus on extracting features from CIR [24; 25; 26; 27; 28; 29]. Early works tend to assess the energy decay and time delay caused by NLOS obstructions. When the LOS path is obstructed, the signal can only reach the receiver through reflection, resulting in a longer propagation distance and more power loss. They use kurtosis and K-Factor to reflect the CIR energy distribution while using Root Mean Delay Spread (RMS) and Mean Excess Delay (MED) to describe the time characteristics [24; 39]. Based on commercial UWB chips, [PERSON] et al. [29] further design thirteen features and select a subset to achieve the best performance. More recent works take advantage of deep neural networks, such as Multi-Layer Perceptions (MLP), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, to learn more complex features from CIR [25; 26; 28]. However, the complex model training also raises the issue of laborious data collection. [PERSON] et al. [27] propose a low-cost NLOS identification method to mitigate the problem. By utilizing the Fresnel zones theory, their approach can work in variable environments without much data collection. UWB devices offer remarkable accuracy due to their extensive bandwidth resources (over 1 GHz), which provide superior time resolution for CIR measurements. Thus, UWB devices are capable of detecting slight changes in the time delay of the received signal, thereby uncovering the tiny differences in propagation distance. However, the high cost of UWB hardware is a defect that cannot be ignored. Although there are already some commercial UWB chips on the market, the deployment of UWB devices is still not as convenient as WiFi. WiFi-based solutions extract features from CSI, which carries both magnitude and phase information. For instance, LiFi [30] explores the statistic parameters of CSI and CIR magnitude. The authors use the Rician-K factor and skewness to capture characteristics of CIR envelope distribution. Their proposed method achieves an identification accuracy of 90.4% by introducing mobility to increase the channel randomness. However, LiFi requires a large sample size of 500-2000 packets for each identification, resulting in a time delay of 1-4 s when transmitting packets at a frequency of 500 Hz.