233 research outputs found
LTE NLOS Navigation and Channel Characterization
Navigation with terrestrial wireless infrastructure is appealing to overcome geometrical limitations of satellite navigation for users in environments with limited sky views. However, terrestrial signals are also prone to multipath that can result in angular and range estimates that are not representative of actual transmitter-receiver geometry. In this paper, some of these propagation effects are quantified for a particular urban non line-of-sight (NLOS) scenario, based on measurements of downlink reference symbols transmitted by a commercial Long Term Evolution (LTE) base station (eNodeB) and received by a massive antenna array mounted on a passenger vehicle. Empirical results indicate that large-scale statistics for a user making multiple passes through the same urban environment look similar when represented in terms of angles and delays despite changes in orientation and drive direction. Additionally, it is demonstrated that multipath effects can be utilized advantageously; it is possible to estimate not only user position but also orientation through wireless fingerprinting
Extended FastSLAM Using Cellular Multipath Component Delays and Angular Information
Opportunistic navigation using cellular signals is appealing for scenarios
where other navigation technologies face challenges. In this paper, long-term
evolution (LTE) downlink signals from two neighboring commercial base stations
(BS) are received by a massive antenna array mounted on a passenger vehicle.
Multipath component (MPC) delays and angle-of-arrival (AOA) extracted from the
received signals are used to jointly estimate the positions of the vehicle,
transmitters, and virtual transmitters (VT) with an extended fast simultaneous
localization and mapping (FastSLAM) algorithm. The results show that the
algorithm can accurately estimate the positions of the vehicle and the
transmitters (and virtual transmitters). The vehicle's horizontal position
error of SLAM fused with proprioception is less than 6 meters after a traversed
distance of 530 meters, whereas un-aided proprioception results in a horizontal
error of 15 meters
Flexible Density-based Multipath Component Clustering Utilizing Ground Truth Pose
Accurate statistical characterization of electromagnetic propagation is necessary for the design and deployment of radio systems. State-of-the-art channel models such as the Enhanced COST 2100 Channel Model utilize the concept of clusters of multipath components, and characterize channels by their inter- and intra-cluster statistics. Automatic clustering algorithms have been proposed in literature, but the subjective nature of the problem precludes any from being deemed objectively correct. In this paper, a new algorithm is proposed, based on density-reachability and ground truth receiver pose, with the explicit focus of extracting clusters for the purpose of channel characterization. Measurements of downlink signals from a commercial LTE base station by a passenger vehicle driving in an urban environment with a massive antenna array on the roof are used to evaluate the repeatability and intuitiveness of the proposed clustering algorithm
SLAM using LTE Multipath Component Delays
Cellular radio based localization can be an important complement or alternative to other localization technologies, as base stations continuously transmit signals of opportunity with beneficial positioning properties. In this paper, we use the long term evolution (LTE) cell-specific reference signal for this purpose. The multipath component delays are estimated by the ESPRIT algorithm, and the estimated multipath component delays of different snapshots are associated by global nearest neighbor with a Kalman filter. Rao-Blackwellized particle filter based simultaneous localization and mapping (SLAM) is then applied to estimate the position of user equipment and that of the base station and virtual transmitters. In a measurement campaign, data from one base station was logged, and the analysis based on the data shows that, at the end of the measurement, the SLAM performance is 11 meters better than that with only inertial measurement unit (IMU)
Wiometrics: Comparative Performance of Artificial Neural Networks for Wireless Navigation
Radio signals are used broadly as navigation aids, and current and future
terrestrial wireless communication systems have properties that make their
dual-use for this purpose attractive. Sub-6 GHz carrier frequencies enable
widespread coverage for data communication and navigation, but typically offer
smaller bandwidths and limited resolution for precise estimation of geometries,
particularly in environments where propagation channels are diffuse in time
and/or space. Non-parametric methods have been employed with some success for
such scenarios both commercially and in literature, but often with an emphasis
on low-cost hardware and simple models of propagation, or with simulations that
do not fully capture hardware impairments and complex propagation mechanisms.
In this article, we make opportunistic observations of downlink signals
transmitted by commercial cellular networks by using a software-defined radio
and massive antenna array mounted on a passenger vehicle in an urban non
line-of-sight scenario, together with a ground truth reference for vehicle
pose. With these observations as inputs, we employ artificial neural networks
to generate estimates of vehicle location and heading for various artificial
neural network architectures and different representations of the input
observation data, which we call wiometrics, and compare the performance for
navigation. Position accuracy on the order of a few meters, and heading
accuracy of a few degrees, are achieved for the best-performing combinations of
networks and wiometrics. Based on the results of the experiments we draw
conclusions regarding possible future directions for wireless navigation using
statistical methods
Extended FastSLAM Using Cellular Multipath Component Delays and Angular Information
Opportunistic navigation using cellular signals is appealing for scenarios where other navigation technologies face challenges. In this paper, long-term evolution (LTE) downlink signals from two neighboring commercial base stations (BS) are received by a massive antenna array mounted on a passenger vehicle. Multipath component (MPC) delays and angle-of-arrival (AOA) extracted from the received signals are used to jointly estimate the positions of the vehicle, transmitters, and virtual transmitters (VT) with an extended fast simultaneous localization and mapping (FastSLAM) algorithm. The results show that the algorithm can accurately estimate the positions of the vehicle and the transmitters (and virtual transmitters). The vehicle’s horizontal position error of SLAM fused with proprioception is less than 6 meters after a traversed distance of 530 meters, whereas un-aided proprioception results in a horizontal error of 15 meters
Urban Navigation with LTE using a Large Antenna Array and Machine Learning
Channel fingerprinting entails associating a point in space with measured properties of a received wireless signal. If the propagation environment for that point in space remains reasonably static with time, then a receiver with no knowledge of its own position experiencing a similar channel in the future might reasonably infer proximity to the original surveyed point. In this article, measurements of downlink LTE Common Reference Symbols from one sector of an eNodeB are used to generate channel fingerprints for a passenger vehicle driving through a dense urban environment without line-of-sight to the transmitter. Channel estimates in the global azimuthal-delay domain are used to create a navigation solution with meter-level accuracy around a city block
High-Resolution Channel Sounding and Parameter Estimation in Multi-Site Cellular Networks
Understanding of electromagnetic propagation properties in real environments is necessary for efficient design and deployment of cellular systems. In this paper, we show a method to estimate high-resolution channel parameters with a massive antenna array in real network deployments. An antenna array mounted on a vehicle is used to receive downlink long-term evolution (LTE) reference signals from neighboring base stations (BS) with mutual interference. Delay and angular information of multipath components is estimated with a novel inter-cell interference cancellation algorithm and an extension of the RIMAX algorithm. The estimated high-resolution channel parameters are consistent with the movement pattern of the vehicle and the geometry of the environment and allow for refined channel modeling and precise cellular positioning
Divide and Adapt: Active Domain Adaptation via Customized Learning
Active domain adaptation (ADA) aims to improve the model adaptation
performance by incorporating active learning (AL) techniques to label a
maximally-informative subset of target samples. Conventional AL methods do not
consider the existence of domain shift, and hence, fail to identify the truly
valuable samples in the context of domain adaptation. To accommodate active
learning and domain adaption, the two naturally different tasks, in a
collaborative framework, we advocate that a customized learning strategy for
the target data is the key to the success of ADA solutions. We present
Divide-and-Adapt (DiaNA), a new ADA framework that partitions the target
instances into four categories with stratified transferable properties. With a
novel data subdivision protocol based on uncertainty and domainness, DiaNA can
accurately recognize the most gainful samples. While sending the informative
instances for annotation, DiaNA employs tailored learning strategies for the
remaining categories. Furthermore, we propose an informativeness score that
unifies the data partitioning criteria. This enables the use of a Gaussian
mixture model (GMM) to automatically sample unlabeled data into the proposed
four categories. Thanks to the "divideand-adapt" spirit, DiaNA can handle data
with large variations of domain gap. In addition, we show that DiaNA can
generalize to different domain adaptation settings, such as unsupervised domain
adaptation (UDA), semi-supervised domain adaptation (SSDA), source-free domain
adaptation (SFDA), etc.Comment: CVPR2023, Highlight pape
Land-cover change detection using paired OpenStreetMap data and optical high-resolution imagery via object-guided Transformer
Optical high-resolution imagery and OpenStreetMap (OSM) data are two
important data sources for land-cover change detection. Previous studies in
these two data sources focus on utilizing the information in OSM data to aid
the change detection on multi-temporal optical high-resolution images. This
paper pioneers the direct detection of land-cover changes utilizing paired OSM
data and optical imagery, thereby broadening the horizons of change detection
tasks to encompass more dynamic earth observations. To this end, we propose an
object-guided Transformer (ObjFormer) architecture by naturally combining the
prevalent object-based image analysis (OBIA) technique with the advanced vision
Transformer architecture. The introduction of OBIA can significantly reduce the
computational overhead and memory burden in the self-attention module.
Specifically, the proposed ObjFormer has a hierarchical pseudo-siamese encoder
consisting of object-guided self-attention modules that extract representative
features of different levels from OSM data and optical images; a decoder
consisting of object-guided cross-attention modules can progressively recover
the land-cover changes from the extracted heterogeneous features. In addition
to the basic supervised binary change detection task, this paper raises a new
semi-supervised semantic change detection task that does not require any
manually annotated land-cover labels of optical images to train semantic change
detectors. Two lightweight semantic decoders are added to ObjFormer to
accomplish this task efficiently. A converse cross-entropy loss is designed to
fully utilize the negative samples, thereby contributing to the great
performance improvement in this task. The first large-scale benchmark dataset
containing 1,287 map-image pairs (1024 1024 pixels for each sample)
covering 40 regions on six continents ...(see the manuscript for the full
abstract
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