UWB is becoming increasingly more available to the general public as part of consumer devices such as smartphones and smartwatches. This opens opportunities for new indoor positioning paradigms because UWB in consumer devices not only supports ranging but also AoA (Angle of Arrival) estimation, meaning dependence on additional positioning infrastructure can be reduced. Since this is a recent development, however, not much research has been conducted on evaluating UWB performance in these consumer devices and how to improve it for better indoor positioning accuracy. To contribute to this research gap, this paper is the first to propose a machine learning solution to AoA accuracy improvement in UWB-equipped iPhones when communicating with DWM3001CDK sensors while in motion. The distinguishing feature of our solution is that, unlike previous works, it uses AoA measurements for training instead of raw CIR (Channel Impulse Response) data, meaning the anchors do not need to be attached to a computer for data collection, which makes the installation of anchors more convenient. In addition, our solution combines machine learning with a collaborative approach based on our positioning vector framework, which further improves AoA error. We compiled a training dataset based on real UWB measurements collected in a large indoor environment. Extensive experiments were conducted to evaluate different machine learning models, and our results show that machine learning can improve the 90th percentile AoA error from about 60° to 11° and thus improve the average direction estimation accuracy to 96.85%.

Despite its high ranging accuracy and secure peer-to-peer ranging, UWB (ultrawide-band) has not been as widely adopted for indoor positioning as other radio technologies, such as WiFi and BLE (Bluetooth Low Energy), due to their high availability. However, UWB chips have recently started to be embedded in consumer devices like smartphones and can often support AoA (Angle of Arrival) estimation, which can aid in the positioning process. To contribute to the emerging research on UWB performance in different chips, this paper presents experimental results on UWB distance and relative angle estimation accuracy over time in UWB-equipped iPhones and a DWM3001CDK chip. iPhones were found to generally display better ranging performance, which declined in mobile scenarios. The UWB developments open new avenues of research for UWB- based collaborative indoor positioning that can reduce dependence on infrastructure, so this paper also proposes a novel dynamic positioning vector framework for mobile UWB-equipped (ultrawide-band) devices, along with new collaborative positioning methods based on the framework. Simulations on the framework's efficacy showed that the new methods can increase positioning coverage to 80% and halve positioning error.

202311 bcch

With the advent of Bluetooth low-energy (BLE)-enabled smartphones, there has been considerable interest in investigating BLE-based distancing/positioning methods (e.g., for social distancing applications). In this article, we present a novel hybrid learning method to support mobile ad-hoc distancing (MAD)/positioning (MAP) using BLE-enabled smartphones. Compared to traditional BLE-based distancing/positioning methods, the hybrid learning method provides the following unique features and contributions. First, it combines unsupervised learning, supervised learning, and genetic algorithms (GAs) for enhancing distance estimation accuracy. Second, unsupervised learning is employed to identify three pseudo channels/clusters for enhanced RSSI data processing. Third, its underlying mechanism is based on a new pattern-inspired approach to enhance the machine learning process. Fourth, it provides a flagging mechanism to alert users if a predicted distance is accurate or not. Fifth, it provides a model aggregation scheme with an innovative 2-D GA to aggregate the distance estimation results of different machine learning models. As an application of hybrid learning for distance estimation, we also present a new MAP scenario with an iterative algorithm to estimate mobile positions in an ad-hoc environment. Experimental results show the effectiveness of the hybrid learning method. In particular, hybrid learning without flagging and with flagging outperforms the baseline by 57% and 65%, respectively, in terms of mean absolute error. By means of model aggregation, a further 4% improvement can be realized. The hybrid learning approach can also be applied to previous work to enhance distance estimation accuracy and provide valuable insights for further research.