The radar experiment connects the geometry of spacetime with an observers measurement of spatial length. We investigate the radar experiment on Finsler spacetimes which leads to a general definition of radar orthogonality and radar length. The directions radar orthogonal to an observer form the spatial equal time surfa…
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48 results for “radar measurements”
Bayesian fusion improves radar target recognition for UAVs.
problem Improving radar target recognition for UAVs using multistatic radar configurations.
method Proposes a fully Bayesian RATR framework using Optimal Bayesian Fusion (OBF) to aggregate classification probability vectors from multiple radars.
result Empirical results show that the OBF method significantly enhances classification accuracy compared to other fusion methods and single radar configurations.
The paper introduces new geometric methods to analyze radar electromagnetic wave statistics.
problem Analyzing spatio-temporal and polarimetric fluctuations of radar electromagnetic waves.
method Using statistical mechanics and Information Geometry, the paper defines a Fréchet barycentre and maximum entropy density for radar measurements.
result New tools for describing radar electromagnetic wave fluctuations, including a distance on covariance matrices.
In multimodal traffic monitoring, we gather traffic statistics for distinct transportation modes, such as pedestrians, cars and bicycles, in order to analyze and improve people's daily mobility in terms of safety and convenience. On account of its robustness to bad light and adverse weather conditions, and inherent spe…
The classification of individual traffic participants is a complex task, especially for challenging scenarios with multiple road users or under bad weather conditions. Radar sensors provide an - with respect to well established camera systems - orthogonal way of measuring such scenes. In order to gain accurate classifi…
Deep Radar Detectorcs.CV
While camera and LiDAR processing have been revolutionized since the introduction of deep learning, radar processing still relies on classical tools. In this paper, we introduce a deep learning approach for radar processing, working directly with the radar complex data. To overcome the lack of radar labeled data, we re…
RETR improves indoor radar perception with a novel transformer model.
problem Indoor radar perception lacks models tailored for multi-view radar settings.
method RETR extends DETR architecture with depth-prioritized feature similarity, tri-plane loss, and radar-to-camera transformation.
result RETR outperforms state-of-the-art methods by 15.38+ AP for object detection and 11.91+ IoU for instance segmentation.
Develops a deep generative model for radar target recognition using HRRP data.
problem Automatic target recognition in radar systems using high-resolution range profiles.
method Recurrent gamma belief network (rGBN) with hybrid stochastic-gradient MCMC and variational inference.
result Efficient and accurate classification with interpretable latent structure.
Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSSeess.SP
Annotating automotive radar data is a difficult task. This article presents an automated way of acquiring data labels which uses a highly accurate and portable global navigation satellite system (GNSS). The proposed system is discussed besides a revision of other label acquisitions techniques and a problem description …
Latent-space metrics for Complex-Valued VAE out-of-distribution detection under radar cluttereess.SP
Study complex-valued VAEs for radar OOD detection.
problem Detecting out-of-distribution signals in complex radar environments.
method Proposed and compared several detection metrics for CVAE.
result CVAE-MSE and latent-based scores outperform ANMF-Tyler.
PiVoT improves real-time multi-object detection and tracking in clutter.
problem Challenges in multi-object detection and tracking from noisy point clouds.
method Variational inference for fast, clutter-resilient multi-object tracking.
result Substantial performance improvement over existing Bayesian trackers.
Paper uses VAEs to detect radar targets in complex noise.
problem Detecting radar targets in compound clutter and thermal noise.
method Proposes a VAE architecture to distinguish radar targets from various noise types.
result The VAE outperforms classical detectors in challenging noise conditions.
For many automated driving functions, a highly accurate perception of the vehicle environment is a crucial prerequisite. Modern high-resolution radar sensors generate multiple radar targets per object, which makes these sensors particularly suitable for the 2D object detection task. This work presents an approach to de…
DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classificationcs.LG
DeepHybrid uses radar data to classify objects accurately.
problem Challenges in classifying objects using automotive radar sensors.
method Combines radar signal processing and Deep Learning; uses neural architecture search.
result Improves object classification performance compared to models using only spectra.
Sparse point observations can provide useful local constraints, but their benefit for radar-like fields depends on the training loss, uncertainty representation, and how observation support is encoded in the model.
problem Improving dense radar-field forecasts with sparse point observations
method A multimodal graph neural network nowcasting system over the Nordic radar domain
result Each source improves a different part of the forecast problem
RL techniques improve radar spectrum sharing in crowded conditions.
problem Optimizing radar performance in congested spectrum environments.
method Comparison of RL algorithms including policy iteration and Deep RL.
result RL techniques outperform traditional SAA methods in radar spectrum sharing.
In the applications related to airborne radars, simulation has always played an important role. This is mainly because of the two fold reason of the unavailability of desired data and the difficulty associated with the collection of data under controlled environment. A simple example will be regarding the collection of…
Study shows latent space OOD detection isn't a reliable proxy for model performance.
problem Evaluating and interpreting deep learning systems on real-world data.
method Empirical investigation of latent space OOD detection and classification accuracy using SAR datasets.
result OOD detection cannot be used as a proxy measure for model performance.
-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequencesstat.ML
New -means method clusters radar image sequences using SPD matrices.
problem Clustering radar image sequences efficiently.
method Developed -means on SPD matrices for non-Euclidean data.
result Effective clustering of radar image sequences via SPD matrices.
Beurling-Selberg Extremization for Dual-Blind Deconvolution Recovery in Joint Radar-Communicationscs.IT
Optimal joint separation condition for radar and communications channels in dual-blind deconvolution.
problem Recovering information from overlaid radar and communications signals with unknown channels.
method Extremal functions from Beurling-Selberg interpolation theory for joint separation, nuclear norm minimization for matrix retrieval, and MUSIC for parameter estimation.
result Guaranteed well-conditioned Vandermonde matrix for MUSIC, validating theoretical findings.
Motion Classification using Kinematically Sifted ACGAN-Synthesized Radar Micro-Doppler Signatureseess.SP
Paper generates synthetic radar signatures for motion classification.
problem Lack of large training datasets for radar-based human activity recognition.
method Adversarial learning for synthetic data generation, kinematic sifting for consistency.
result 93% overall accuracy achieved on diverse aspect angles.
Develops a universal waveform selection scheme for radar tracking.
problem Optimal waveform selection for target tracking in active sensors.
method Uses reinforcement learning and universal source coding techniques.
result Achieves optimal waveform selection for any radar scene modeled as a Markov process.
SVDD and Deep SVDD improve radar target detection in clutter.
problem Clutter and thermal noise degrade classical radar detection methods.
method Support Vector Data Description (SVDD) and Deep SVDD for one-class learning.
result SVDD and Deep SVDD outperform traditional methods on simulated radar data.
New method recovers radar and communication signals from overlaid data.
problem Recover radar and communication signals from overlaid data with unknown parameters.
method Propose minimizing the sum of multivariate atomic norms (SoMAN) for multi-antenna receiver.
result Minimum number of samples and antennas required for perfect recovery is logarithmically dependent on the maximum of radar targets and communications paths.
This paper considers object detection and 3D estimation using an FMCW radar. The state-of-the-art deep learning framework is employed instead of using traditional signal processing. In preparing the radar training data, the ground truth of an object orientation in 3D space is provided by conducting image analysis, of w…
In this paper, mm-Pose, a novel approach to detect and track human skeletons in real-time using an mmWave radar, is proposed. To the best of the authors' knowledge, this is the first method to detect >15 distinct skeletal joints using mmWave radar reflection signals. The proposed method would find several applications …
In radar systems, unimodular (or constant-modulus) waveform design plays an important role in achieving better clutter/interference rejection, as well as a more accurate estimation of the target parameters. The design of such sequences has been studied widely in the last few decades, with most design algorithms requiri…
A review of radar-based nowcasting of precipitation and applicable machine learning techniquesphysics.ao-ph
This paper reviews radar-based nowcasting techniques for short-term weather predictions.
problem Short-term weather predictions for adverse events like heavy rain and flooding.
method Analysis of existing radar-based nowcasting techniques and machine learning approaches.
result New partnerships between environmental science and machine learning can improve nowcasting.
mmFall detects falls using mmWave radar and a hybrid VRAE, achieving high accuracy.
problem Detecting falls accurately and privately using mmWave radar.
method Uses mmWave radar for body point cloud and centroid, combined with a hybrid VRAE for anomaly detection.
result Achieves 98% fall detection with 2 false alarms out of 50 falls.
A fast method for estimating radar amplitude density parameters.
problem Accurate estimation of amplitude density function parameters in radar applications.
method Projecting amplitude data onto horizontal and vertical axes, then using MLE for -stale distribution parameters.
result The average of computed MLEs based on two projections is a fast and accurate estimator for amplitude distribution parameters.
RADAR uses diffusion models to detect anomalies without reconstruction, improving accuracy and efficiency.
problem Challenges in anomaly detection and segmentation, especially in real-time applications.
method RADAR uses attention-based diffusion models to directly produce anomaly maps from the diffusion process, bypassing reconstruction.
result RADAR improves F1 score by 7% on MVTec-AD and 13% on 3D-printed material compared to state-of-the-art methods.
Direction of arrival (DoA) estimation of targets improves with the number of elements employed by a phased array radar antenna. Since larger arrays have high associated cost, area and computational load, there is recent interest in thinning the antenna arrays without loss of far-field DoA accuracy. In this context, a c…
To address potential gaps noted in patient monitoring in the hospital, a novel patient behavior detection system using mmWave radar and deep convolution neural network (CNN), which supports the simultaneous recognition of multiple patients' behaviors in real-time, is proposed. In this study, we use an mmWave radar to t…
New filters improve radar target inference in complex scenarios.
problem Improving radar target inference in highly non-linear system models.
method Developed inverse cubature Kalman filter (I-CKF), inverse quadrature Kalman filter (I-QKF), and inverse cubature-quadrature Kalman filter (I-CQKF) for non-linear systems.
result Numerical experiments show improved estimation accuracy compared to existing methods.
A machine-learning method speeds up RIS design by predicting reflection coefficients.
problem Extensive full-wave EM simulations are time-consuming for RIS design.
method Combining MLP and dual-port network to develop a fast model.
result The proposed method significantly reduces the time for RIS design.
Many sensors, such as range, sonar, radar, GPS and visual devices, produce measurements which are contaminated by outliers. This problem can be addressed by using fat-tailed sensor models, which account for the possibility of outliers. Unfortunately, all estimation algorithms belonging to the family of Gaussian filters…
Study forecasts sub-city real estate prices weekly using radar and news sentiment.
problem Limited availability of reliable real estate price indicators at neighborhood and long horizons.
method Combining satellite radar signals and news sentiment to forecast sub-city real estate prices.
result The multimodal model reduces mean absolute error by 35% at long horizons (26-34 weeks).
LFlows model fluid densities and velocities using invertible maps that satisfy the continuity equation.
problem Modeling fluid densities and velocities continuously in space and time.
method LFlows are based on invertible maps that satisfy the continuity equation, derived from classical theory of Lagrangian flows for smooth vector fields.
result LFlows show higher predictive accuracy in density modeling tasks compared to competing models in 2D and 3D.
A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecastsphysics.ao-ph
Generative deep learning improves precipitation forecasts by adding resolution.
problem Inaccurate and unreliable precipitation forecasts due to unresolved processes.
method Applying GANs to super-resolve low-resolution weather model data using radar measurements.
result GANs and VAE-GANs produce high-resolution precipitation maps with better statistical properties than existing methods.
Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensemblescs.LG
Radar-based road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to recover by subsequent signal processing. In this article, classifier ensembles origin…
CVAE detects weak complex signals in maritime radar, improving detection over classical methods.
problem Detecting weak complex-valued signals in non-Gaussian, range-varying interference.
method Complex-valued Variational AutoEncoder (CVAE) trained on clutter-plus-noise, whitening, ANMF fusion.
result CVAE yields higher detection probability Pd at matched false-alarm rate Pfa, especially with whitening.
In this article, we analyze the microlocal properties of the linearized forward scattering operator and the normal operator (where is the adjoint of ) which arises in Synthetic Aperture Radar imaging for the common midpoint acquisition geometry. When is applied to the scattered d…
In this paper, a generalized multivariate Student-t mixture model is developed for classification and clustering of Low Probability of Intercept radar waveforms. A Low Probability of Intercept radar signal is characterized by a pulse compression waveform which is either frequency-modulated or phase-modulated. The propo…
Knowledge about frequency and location of snow avalanche activity is essential for forecasting and mapping of snow avalanche hazard. Traditional field monitoring of avalanche activity has limitations, especially when surveying large and remote areas. In recent years, avalanche detection in Sentinel-1 radar satellite im…
Models for predicting aircraft motion are an important component of modern aeronautical systems. These models help aircraft plan collision avoidance maneuvers and help conduct offline performance and safety analyses. In this article, we develop a method for learning a probabilistic generative model of aircraft motion i…
This paper investigates the anti-jamming performance of a cognitive radar under a partially observable Markov decision process (POMDP) model. First, we obtain an explicit expression for uncertainty of jammer dynamics, which paves the way for illuminating the performance metric of probability of being jammed for the rad…
Paper develops a deforestation detection system using optical and SAR data.
problem Detecting tree-loss in dense forests using satellite data.
method Combines optical and SAR data, uses KL expansion for anomaly detection, and Hidden Markov Model for classification.
result Hybrid method achieves high accuracy and robustness in sparse optical data.
We consider the nonlinear Kalman filtering problem using Kullback-Leibler (KL) and -divergence measures as optimization criteria. Unlike linear Kalman filters, nonlinear Kalman filters do not have closed form Gaussian posteriors because of a lack of conjugacy due to the nonlinearity in the likelihood. In this paper …