# 1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
## 1 Introduction  
In recent years, the need for precise positioning has increased across various sectors. While satellite-based positioning systems are effective outdoors, their limited signal penetration makes them unsuitable for indoor navigation, prompting the development of indoor positioning systems [1, 2]. Advances in sensing technologies have introduced methods like WIFI, RSSI, and Ultra-Wideband (UWB) positioning, which are widely utilized in the internet of things (IoT), smart buildings, healthcare, and industrial navigation [3]. UWB technology is particularly notable for its high temporal accuracy, low power consumption, and strong penetration capabilities, making it ideal for precise indoor applications [4]. However, UWB indoor positioning systems (IPS) face significant challenges due to their susceptibility to Non-Line-of-Sight (NLoS) conditions, which greatly reduce positioning accuracy [5]. As shown in Figure 1, under NLoS conditions, signals from the transmitter (TX) are prone to refraction, reflection, and diffraction. Only a small portion of the signal can penetrate obstacles and reach the receiver (RX) directly. The different arrival times and phases of these signal components can cause distortion or errors in the original signal, a phenomenon known as the multipath effect [6]. This issue is particularly severe in complexindoor environments with significant occlusion. Accurate distance estimation in UWB networks relies on measuring the time of flight (ToF) under optimal Line-of-Sight (LoS) conditions. Nevertheless, NLoS conditions, prevalent in indoor settings, distort signal delays, leading to positive biases in ToF estimates and subsequent distance measurements [7]. Most UWB positioning algorithms, such as trilateration [8] and the least squares method [9], assume ideal LoS conditions between anchors and tags, which can result in significant errors when NLoS and multipath components are present [10]. Therefore, identifying and mitigating the negative impact of NLoS is crucial. Recent advancements in NLoS identification techniques for UWB positioning have greatly enhanced accuracy in indoor environments [11]. Key approaches include statistical hypothesis testing [12], machine learning (ML) [13], and deep learning (DL) methods [14]. Statistical hypothesis testing evaluates the likelihood of NLoS conditions based on signal characteristics [12], analyzing the statistical properties of the received signal metrics, such as the signal strength or time-of-flight, to detect deviations indicative of NLoS propagation. ML algorithms rely on extracting hand-crafted features from the channel impulse response (CIR) data and training models using labeled datasets to differentiate between LoS and NLoS scenarios [13], employing techniques like decision trees, support vector machines (SVMs), and ensemble methods to identify patterns in signal data characteristic of NLoS conditions. DL techniques, including recurrent neural networks (RNNs) and convolutional neural networks (CNNs), directly process raw UWB signal data [14], automatically learning hierarchical features that capture complex temporal and spatial dependencies. For instance, CNNs extract spatial features through convolutional layers, while RNNs, particularly Long Short-Term memory (LSTM) networks, capture sequential dependencies, making them well-suited for time-series data inherent in UWB signals [15]. These advancements are crucial for enhancing the accuracy and reliability of UWB ranging and positioning systems in dynamic surroundings with obstacles, effectively identifying and mitigating NLoS conditions to ensure robust performance [16]. Although binary NLoS recognition methods have achieved high accuracy, they struggle to differentiate subtle variations in complex indoor environments. To address this, we introduce an innovative NLoS recognition model based on a low-power UWB chip, named the 1D-ConvLSTM-Attention network (1D-CLANet).