Wireless high-accuracy positioning has recently attracted growing research interest due to diversified nature of applications such as industrial asset tracking, autonomous driving, process automation, and many more. However, obtaining a highly accurate location information is hampered by challenges due to the radio environment. A major source of error for time-based positioning methods is inaccurate time-of-arrival (ToA) or range estimation. Existing machine learning-based solutions to mitigate such errors rely on propagation environment classification hindered by a low number of classes, employ a set of features representing channel measurements only to a limited extent, or account for only device-specific proprietary methods of ToA estimation. In this paper, we propose convolutional neural networks (CNNs) to estimate and mitigate the errors of a variety of ToA estimation methods utilizing channel impulse responses (CIRs). Based on real-world measurements from two independent campaigns, the proposed method yields significant improvements in ranging accuracy (up to 37%) of the state-of-the-art ToA estimators, often eliminating the need of optimizing the underlying conventional methods.
Positioning is becoming a key component in many Internet of Things (IoT) applications. The main challenges and limitations are the narrow bandwidth, low power and low cost which reduces the accuracy of the time of arrival (TOA) estimation. In this paper, we consider the positioning scenario of Narrowband IoT (NB-IoT) that can benefit from observed time difference of arrival (OTDOA). By applying the deep learning based technique, we explore the generalization and feature extraction abilities of neural networks to tackle the aforementioned challenges. As demonstrated in the numerical experiments…
This paper explores an integrated approach for improved sensing and positioning with applications in air traffic management (ATM) and in the Advanced Surface Movement Guidance and Control System (A-SMGCS). The integrated approach includes the synergy of 3D Vector Antenna with the novel time-of-arrival and angle-of-arrival estimate methods for accurate positioning, combining the sensing on the sub-6GHz and mmWave spectrum for the enhanced non-cooperative surveillance. For the positioning scope, both uplink and downlink 5G reference signals are investigated and their performance is evaluated. Fo…
The continuously increasing bandwidth and antenna aperture available in wireless networks laid the foundation for developing competitive positioning solutions relying on communications standards and hardware. However, poor propagation conditions such as non-line of sight (NLOS) and rich multipath still pose many challenges due to outlier measurements that significantly degrade the positioning performance. In this work, we introduce an iterative positioning method that reweights the time of arrival (ToA) and angle of arrival (AoA) measurements originating from multiple locators in order to effi…
CSI-based Positioning in Massive MIMO systems using Convolutional Neural Networks
研究了利用大规模多输入多输出（MaMIMO）系统的信道状态信息（CSI）进行用户定位的性能。为了从CSI中推断用户的位置，设计了一个卷积神经网络，并通过一个新的数据集进行评估。该数据集包含使用三种不同天线拓扑的室内MaMIMO CSI测量，覆盖2.5 m x 2.5 m的室内区域。我们表明，我们可以训练一个卷积神经网络（CNN）模型来估计该区域内的用户的位置，平均误差小于半个波长。此外，一旦模型在给定的场景和天线拓扑结构上进行了训练，迁移学习就用于将所获得的知识重新用于具有显著不同的天线拓扑结构和配置的另一场景。我们的研究结果表明，它是可能的进一步训练的CNN只使用少量的额外标记的样本的新的拓扑结构。这种迁移学习方法能够达到准确的结果，为CNN支持的基于CSI的实用定位系统铺平了道路。
The ubiquity, large bandwidth, and spatial diversity of the fifth generation (5G) cellular signal render it a promising candidate for accurate positioning in indoor environments where the global navigation satellite system (GNSS) signal is absent. In this paper, a joint angle and delay estimation (JADE) scheme is designed for 5G picocell base stations (gNBs) which addresses two crucial issues to make it both effective and efficient in realistic indoor environments. Firstly, the direction-dependence of the array modeling error for picocell gNB as well as its impact on JADE is revealed. This err…
Jožef Stefan Institute（约热夫·斯蒂芬研究所）；Jožef Stefan International Postgraduate School（约热夫·斯蒂芬国际研究生院）
Multiple-input multiple-output (MIMO) is an enabling technology to meet the growing demand for faster and more reliable communications in wireless networks with a large number of terminals, but it can also be applied for position estimation of a terminal exploiting multipath propagation from multiple antennas. In this paper, we investigate new convolutional neural network (CNN) structures for exploiting MIMO-based channel state information (CSI) to improve indoor positioning. We evaluate and compare the performance of three variants of the proposed CNN structure to five NN structures proposed …
In this paper, we propose a comprehensive framework to jointly analyze the angle estimation error and design the three-dimensional (3D) positioning algorithm for a millimeter wave (mmWave) positioning system. First, we estimate the angles of arrival (AoAs) at the anchors by applying the two-dimensional discrete Fourier transform (2D-DFT) algorithm. Based on the property of the 2D-DFT algorithm, the angle estimation error is analyzed in terms of probability density functions (PDF). The analysis shows that the derived angle estimation error is non-Gaussian, which is different from the existing w…
For radio-based time-of-arrival (TOA) positioning systems applied in harsh environments, obstacles in the surroundings and on the vehicle itself will block the signals from the anchors, reduce the number of available TOA measurements and thus degrade the localization performance. Conventional multi-antenna positioning technique requires a good initialization to avoid local minima, and suffers from location ambiguity due to insufficient number of TOA measurements and/or poor geometry of anchors at a single epoch. A new initialization method based on semidefinite programming (SDP), namely MEMA-S…
In a time-of-arrival (TOA) or pseudorange based positioning system, user location is obtained by observing multiple anchor nodes (AN) at known positions. Utilizing more than one positioning systems, e.g., combining Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS), brings better positioning accuracy. However, ANs from two systems are usually synchronized to two different clock sources. Different from single-system localization, an extra user-to-system clock offset needs to be handled. Existing dual-system methods either have high computational complexity or sub-optim…
Reconfigurable intelligent surface (RIS) is a promising technique for millimeter wave (mmWave) positioning systems. In this paper, we consider multiple mobile users (MUs) positioning problem in the multiple-input multiple-output (MIMO) time-division duplex (TDD) mmWave systems aided by the RIS. We derive the expression for the space-time channel response vector (STCRV) as a novel type of fingerprint. The STCRV consists of the multipath channel characteristics, e.g., time delay and angle of arrival (AOA), which is related to the position of the MU. By using the STCRV as input, we propose a nove…
Impact of Preprocessing on Neural Network-Based RSS/AoA Positioning
Instituto de Telecomunicações（电信研究所）；Universidade Nova de Lisboa（里斯本新大学）；UNINOVA-CTS – Center of Technology and Systems, NOVA School of Science and Technology（NOVA科学技术学院UNINOVA-CTS技术与系统中心）；COPELABS, ECATI, Lusófona University（卢索菲纳大学COPELABS和ECATI）；Innov’Com Laboratory, Sup’Com, University of Carthage（迦太基大学Sup’Com Innov’Com实验室）；ISCTE-Instituto Universitário de Lisboa（里斯本ISCTE大学）；Nokia（诺基亚）
On the of the applications in the realm of the Internet-of-Things (IoT) is real-time localization of assets in specific application environments where satellite based global positioning is unviable. Numerous approaches for localization relying on wireless sensor mesh systems have been evaluated, but the recent Bluetooth Low Energy (BLE) 5.1 direction finding features based on Angle-of-Arrival (AoA) promise a low-cost solution for this application. In this paper, we present an implementation of a BLE 5.1 based circular antenna array, and perform two experimental evaluations over the quality of …
Owing to the ubiquity of cellular communication signals, positioning with the fifth generation (5G) signal has emerged as a promising solution in global navigation satellite system-denied areas. Unfortunately, although the widely employed antenna arrays in 5G remote radio units (RRUs) facilitate the measurement of the direction of arrival (DOA), DOA-based positioning performance is severely degraded by array errors. This paper proposes an in-situ calibration framework with a user terminal transmitting 5G reference signals at several known positions in the actual operating environment and the a…
Indoor positioning is one of the core technologies of Internet of Things (IoT) and artificial intelligence (AI), and is expected to play a significant role in the upcoming era of AI. However, affected by the complexity of indoor environments, it is still highly challenging to achieve continuous and reliable indoor positioning. Currently, 5G cellular networks are being deployed worldwide, the new technologies of which have brought the approaches for improving the performance of wireless indoor positioning. In this paper, we investigate the indoor positioning under the 5G new radio (NR), which h…
This paper deals with the problem of localization in a cellular network in a dense urban scenario. Global Navigation Satellite Systems (GNSS) typically perform poorly in urban environments, where the likelihood of line-of-sight conditions is low, and thus alternative localization methods are required for good accuracy. We present LocUNet: A deep learning method for localization, based merely on Received Signal Strength (RSS) from Base Stations (BSs), which does not require any increase in computation complexity at the user devices with respect to the device standard operations, unlike methods …
In a time division broadcast positioning system (TDBPS), a user device (UD) determines its position by obtaining sequential time-of-arrival (TOA) or pseudorange measurements from signals broadcast by multiple synchronized base stations (BSs). The existing localization method using sequential pseudorange measurements and a linear clock drift model for the TDPBS, namely LSPM-D, does not compensate the position displacement caused by the UD movement and will result in position error. In this paper, depending on the knowledge of the UD velocity, we develop a set of optimal localization methods for…
In this paper, we exploit the maximum correntropy criterion (MCC) to robustify the traditional time-difference-of-arrival (TDOA) location estimator in the presence of non-line-of-sight (NLOS) propagation conditions. For the sake of statistical efficiency, the correntropy-based robust loss is imposed on the underlying time-of-arrival composition via joint estimation of the source position and onset time, instead of the TDOA counterpart generated in the postprocessing of sensor-collected timestamps. We then employ a neurodynamic optimization approach to tackle the highly nonconvex MCC formulatio…
Intelligent reflecting surface (IRS) offers a cost effective solution to link blockage problem in mmWave communications, and the prerequisite of which is the accurate estimation of (1) the optimal beams for base station/access point (BS/AP) and mobile terminal (MT), (2) the optimal reflection patterns for IRSs, and (3) link blockage. In this paper, we carry out beam training design for IRSs assisted mmWave communications to estimate the aforementioned parameters. To acquire the optimal beams and reflection patterns, we firstly perform random beamforming and maximum likelihood estimation to est…
In this paper, we introduce the concept of partially-connected Receiving Reconfigurable Intelligent Surfaces (R-RISs), which refers to metasurfaces designed to efficiently sense electromagnetic waveforms impinging on them, and perform localization of the users emitting them. The presented R-RIS hardware architecture comprises subarrays of meta-atoms, with each of them incorporating a waveguide assigned to direct the waveforms reaching its meta-atoms to a reception Radio-Frequency (RF) chain, enabling signal/channel parameter estimation. We particularly focus on the scenarios where the user is …
Multi-point vehicular positioning is one essential operation for autonomous vehicles. However, the state-of-the-art positioning technologies, relying on reflected signals from a target (i.e., RADAR and LIDAR), cannot work without line-of-sight. Besides, it takes significant time for environment scanning and object recognition with potential detection inaccuracy, especially in complex urban situations. Some recent fatal accidents involving autonomous vehicles further expose such limitations. In this paper, we aim at overcoming these limitations by proposing a novel relative positioning approach…
Universitat Jaume I（海梅一世大学）；Tampere University（坦佩雷大学）；Universidade do Minho（米尼奥大学）
Indoor Positioning based on Machine Learning has drawn increasing attention both in the academy and the industry as meaningful information from the reference data can be extracted. Many researchers are using supervised, semi-supervised, and unsupervised Machine Learning models to reduce the positioning error and offer reliable solutions to the end-users. In this article, we propose a new architecture by combining Convolutional Neural Network (CNN), Long short-term memory (LSTM) and Generative Adversarial Network (GAN) in order to increase the training data and thus improve the position accurac…
