A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform

Localization systems based on ultra-wide band (UWB) measurements can have unsatisfactory performance in harsh environments due to the presence of non-line-of-sight (NLOS) errors. Learning-based methods for error mitigation have shown great performance improvement via directly exploiting the wideband waveform instead of handcrafted features. However, these methods require data samples fully labeled with actual measurement errors for training, which leads to time-consuming data collection. In this paper, we propose a semi-supervised learning method based on variational Bayes for UWB ranging error mitigation. Combining deep learning techniques and statistic tools, our method can efficiently accumulate knowledge from both labeled and unlabeled data samples. Extensive experiments illustrate the effectiveness of the proposed method under different supervision rates, and the superiority compared to other fully supervised methods even at a low supervision rate.Comment: 5 pages, 3 figures, Published in: MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM

Massive MIMO-based Localization and Mapping Exploiting Phase Information of Multipath Components

In this paper, we present a robust multipath-based localization and mapping framework that exploits the phases of specular multipath components (MPCs) using a massive multiple-input multiple-output (MIMO) array at the base station. Utilizing the phase information related to the propagation distances of the MPCs enables the possibility of localization with extraordinary accuracy even with limited bandwidth. The specular MPC parameters along with the parameters of the noise and the dense multipath component (DMC) are tracked using an extended Kalman filter (EKF), which enables to preserve the distance-related phase changes of the MPC complex amplitudes. The DMC comprises all non-resolvable MPCs, which occur due to finite measurement aperture. The estimation of the DMC parameters enhances the estimation quality of the specular MPCs and therefore also the quality of localization and mapping. The estimated MPC propagation distances are subsequently used as input to a distance-based localization and mapping algorithm. This algorithm does not need prior knowledge about the surrounding environment and base station position. The performance is demonstrated with real radio-channel measurements using an antenna array with 128 ports at the base station side and a standard cellular signal bandwidth of 40 MHz. The results show that high accuracy localization is possible even with such a low bandwidth.Comment: 14 pages (two columns), 13 figures. This work has been submitted to the IEEE Transaction on Wireless Communications for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A Variational Learning Approach for Concurrent Distance Estimation and Environmental Identification

Wireless propagated signals encapsulate rich information for high-accuracy localization and environment sensing. However, the full exploitation of positional and environmental features as well as their correlation remains challenging in complex propagation environments. In this paper, we propose a methodology of variational inference over deep neural networks for concurrent distance estimation and environmental identification. The proposed approach, namely inter-instance variational auto-encoders (IIns-VAEs), conducts inference with latent variables that encapsulate information about both distance and environmental labels. A deep learning network with instance normalization is designed to approximate the inference concurrently via deep learning. We conduct extensive experiments on real-world datasets and the results show the superiority of the proposed IIns-VAE in both distance estimation and environmental identification compared to conventional approaches

Cooperative Cellular Localization with Intelligent Reflecting Surface: Design, Analysis and Optimization

Autonomous driving and intelligent transportation applications have dramatically increased the demand for high-accuracy and low-latency localization services. While cellular networks are potentially capable of target detection and localization, achieving accurate and reliable positioning faces critical challenges. Particularly, the relatively small radar cross sections (RCS) of moving targets and the high complexity for measurement association give rise to weak echo signals and discrepancies in the measurements. To tackle this issue, we propose a novel approach for multi-target localization by leveraging the controllable signal reflection capabilities of intelligent reflecting surfaces (IRSs). Specifically, IRSs are strategically mounted on the targets (e.g., vehicles and robots), enabling effective association of multiple measurements and facilitating the localization process. We aim to minimize the maximum Cram\'er-Rao lower bound (CRLB) of targets by jointly optimizing the target association, the IRS phase shifts, and the dwell time. However, solving this CRLB optimization problem is non-trivial due to the non-convex objective function and closely coupled variables. For single-target localization, a simplified closed-form expression is presented for the case where base stations (BSs) can be deployed flexibly, and the optimal BS location is derived to provide a lower performance bound of the original problem ...Comment: 14 pages, This work has been submitted to IEEE for possible publicatio

Resource Allocation for Joint Communication and Positioning in Mmwave Ad Hoc Networks

Joint communication and positioning will be a critical driver in future wireless networks for emerging application areas. Supporting mobility, Ad-hoc networks can freely and dynamically self-organize an arbitrary and temporary network topology without any pre-existing infrastructure. Combined with millimeter-wave (mmWave), Ad-hoc networks can construct communication links with less time and higher directivity due to directional antennas and building blockage. The wide spectrum of mmWave could provide a high-oriented channel for positioning, which is significant for multi-user conditions. In this paper, we concentrate on high-efficiency algorithms to allocate spectrum and power to different services and achieve a performance tradeoff between the communication and positioning process. Besides, the severe interference between users would degrade the actual system performance. To address these challenges, this paper proposes an optimal clustering algorithm based on the mmWave Line of Sight (LoS) probability to form two different sub-nets for communication and positioning services, respectively. Then, the available spectrum resources are divided into two parts for the above sub-nets under the Filtered-Orthogonal Frequency Division Multiplexing (F-OFDM) technique, which could design sub-bands independently. Finally, we proposed an optimal algorithm to allocate sub-bands and power to improve the performance of the communication sub-net while guaranteeing the positioning performance in the corresponding sub-net. Numeric simulation results demonstrate that the proposed resource allocation algorithm could achieve better performance both in the communication and position process