Ultra-wideband Indoor Positioning System Research Articles

1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System

Ultra-Wideband (UWB) technology is crucial for indoor localization systems due to its high accuracy and robustness in multipath environments. However, Non-Line-of-Sight (NLoS) conditions can cause UWB signal distortion, significantly reducing positioning accuracy. Thus, distinguishing between NLoS and LoS scenarios and mitigating positioning errors is crucial for enhancing UWB system performance. This research proposes a novel 1D-ConvLSTM-Attention network (1D-CLANet) for extracting UWB temporal channel impulse response (CIR) features and identifying NLoS scenarios. The model combines the convolutional neural network (CNN) and Long Short-Term memory (LSTM) architectures to extract temporal CIR features and introduces the Squeeze-and-Excitation (SE) attention mechanism to enhance critical features. Integrating SE attention with LSTM outputs boosts the model’s ability to differentiate between various NLoS categories. Experimental results show that the proposed 1D-CLANet with SE attention achieves superior performance in differentiating multiple NLoS scenarios with limited computational resources, attaining an accuracy of 95.58%. It outperforms other attention mechanisms and the version of 1D-CLANet without attention. Compared to advanced methods, the SE-enhanced 1D-CLANet significantly improves the ability to distinguish between LoS and similar NLoS scenarios, such as human obstructions, enhancing overall recognition accuracy in complex environments.

A Succinct Method for Non-Line-of-Sight Mitigation for Ultra-Wideband Indoor Positioning System.

Ultra-wideband (UWB) is a promising indoor position technology with centimetre-level positioning accuracy in line-of-sight (LOS) situations. However, walls and other obstacles are common in an indoor environment, which can introduce non-line-of-sight (NLOS) and deteriorate UWB positioning accuracy to the meter level. This paper proposed a succinct method to identify NLOS induced by walls and mitigate the error for improved UWB positioning with NLOS. First, NLOS is detected by a sliding window method, which can identify approximately 90% of NLOS cases in a harsh indoor environment. Then, a delay model is designed to mitigate the error of the UWB signal propagating through a wall. Finally, all the distance measurements, including LOS and NLOS, are used to calculate the mobile UWB tag position with ordinary least squares (OLS) or weighted least squares (WLS). Experiment results show that with correct NLOS indentation and delay model, the proposed method can achieve positioning accuracy in NLOS environments close to the level of LOS. Compared with OLS, WLS can further optimise the positioning results. Correct NLOS indentation, accurate delay model and proper weights in the WLS are the keys to accurate UWB positioning in NLOS environments.

An ultrawideband indoor positioning system with distributed ranging between tags

The aim of the work is to develop methodologies based on ultra-wideband positioning to create systems for the mutual positioning of moving objects. For this purpose, positioning errors in ultra-wideband radio navigation in the presence and absence of a direct signal propagation line between the radio tag of a mobile object and a base station are shown in the article. In presence of difficulties, pseudorange measurements acquire a displacement error. The construction of positioning systems using the ranges between radio tags (interpersonal channel) is proposed in the article. Construction of schemes based on radio channel models of copper mines shows that there is no error in the displacement of the RFID tag range, which is defined as the sum of the ranges by the intermediate RFID tag. With a low speed of movement of an intermediate RFID tag and a high frequency of range measurements, the intermediate marker can be represented as a fixed base station. The broadcast format of RFID signals will allow avoiding collisions on the air.

Tracking group housed sows with an ultra-wideband indoor positioning system: A feasibility study

Automated tracking of indoor housed farm animals is gaining an increasing interest in farming practice and research for monitoring animal behaviour and health. Several positioning solutions have become commercially available for different animal species, but the insight on how these systems perform in pig facilities is still limited. In this study the feasibility of tracking sows in a barn using an ultra-wideband (UWB) based indoor positioning system was investigated. The system consisted of 7 base stations and multiple tracker tags, and the determination of tags' positions was based on time of arrival (ToA). For locating stationary tags the system achieved 0.37 m accuracy at 1 m height and 0.50 m accuracy at 0.3 m height. While tracking moving tags at 1 m height 0.38 m accuracy was achieved. Three filters were tested to further improve the positioning performance. The median filter had the highest improvement on locating stationary and moving tags. The bilateral filter had a balanced capability of uncovering complex moving trajectories. The double exponential moving average filter was more suited when real-time updates were required. The overall results showed that tracking group housed sows inside a pig barn was feasible using the UWB positioning system. • A UWB positioning system achieved <0.5 m positioning accuracy using in a sow barn. • The positioning accuracy was dependent on the location and height of tracker tags. • Filters were more effective for improving tracking precision than accuracy. • The choice of filters depends on the motion characteristics and the application.