New k-means method clusters radar image sequences using SPD matrices.
problem Clustering radar image sequences efficiently.
method Developed k-means on SPD matrices for non-Euclidean data. result Effective clustering of radar image sequences via SPD matrices.
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.
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…
DL improves precipitation nowcasting from radar images.
problem Precise short-term precipitation predictions for extreme weather adaptation.
method Image-to-image translation using UNET CNN, compared to optical flow, persistence, and HRRR.
result UNET-based DL outperforms traditional models in 1 km x 1 km, 1 hour precipitation nowcasting.
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…
mm-Pose detects human skeletons in real-time using mmWave radar and CNNs.
problem Real-time human skeletal posture estimation in various scenarios.
method mmWave radar, radar-to-image representation, forked CNN architecture.
result Accurate predictions for human skeletal joints in 3D space.
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.
In this article, we analyze the microlocal properties of the linearized forward scattering operator F and the normal operator F∗F (where F∗ is the L2 adjoint of F) which arises in Synthetic Aperture Radar imaging for the common midpoint acquisition geometry. When F∗ is applied to the scattered d…
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…
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…
Paper tackles anomaly detection in SAR images without labeled data.
problem Anomaly detection in SAR images with speckle noise and spatial correlation issues.
method Self-supervised deep learning approach: despeckling, adversarial autoencoder, change detection.
result Method outperforms conventional algorithms in detecting anomalies in SAR images.
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 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…
New method maps land cover using radar and optical satellite images.
problem Efficiently exploiting multiple sources of information for land cover mapping.
method Deep learning framework with attention mechanism and pretraining strategy.
result Attention mechanism and extended RNN model outperform competitors.
Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combinatio…
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.
GMM uses mmWave radar to classify traffic modes in poor lighting.
problem Classifying traffic modes in poor lighting conditions.
method GMM on mmWave radar point clouds.
result Good segmentation performance in pedestrian and car classification.
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 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.
Novel radar waveform design for autonomous vehicles.
problem Efficient radar waveform design in time-varying environments.
method Hybrid model-driven and data-driven architecture.
result Adaptive unimodular waveform design for real-time scenarios.
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.
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.
Kernel K-means clusters probability distributions.
problem Clustering a sample of probability distributions.
method Mapping distributions to kernel mean embeddings in RKHS, then applying K-means.
result Effective unsupervised classification of probability distributions.
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.
CNNs improve InSAR image denoising and coherence estimation.
problem Noise in InSAR imagery corrupts ground movement estimates.
method Autoencoder CNN architectures for denoising and preprocessing.
result Proposed method outperforms four established methods.
System detects multiple patients' behaviors in real-time using mmWave radar and CNN.
problem Real-time patient behavior monitoring in hospitals.
method Used mmWave radar for tracking and collecting Doppler patterns. Created a three-layer CNN model for behavior classification.
result System achieved very good inference accuracy in predicting patient behaviors in real-time.
Polarimetric Synthetic Aperture Radar (PolSAR) images are establishing as an important source of information in remote sensing applications. The most complete format this type of imaging produces consists of complex-valued Hermitian matrices in every image coordinate and, as such, their visualization is challenging. Th…
Deep Learning is gaining traction with geophysics community to understand subsurface structures, such as fault detection or salt body in seismic data. This study describes using deep learning method for iceberg or ship recognition with synthetic aperture radar (SAR) data. Drifting icebergs pose a potential threat to ac…
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.
Generative adversarial networks generate realistic, time-evolving high-resolution atmospheric fields.
problem Improving spatial resolution of low-resolution atmospheric images.
method Recurrent, stochastic super-resolution GAN for generating ensembles of time-evolving high-resolution atmospheric fields.
result The GAN produces realistic, temporally consistent super-resolution sequences for radar-measured precipitation and cloud optical thickness.
This paper improves radar performance against jammers using RL.
problem Improving radar performance against jammers.
method Reinforcement Learning (RL) with Deep Q-Network (DQN) and Long Short Term Memory (LSTM) networks.
result Softmax operator improves RL algorithm performance.
Study on deep learning for speckle noise reduction in imaging modalities.
problem Multiplicative speckle noise challenges conventional deep learning methods for speckle denoising.
method Likelihood-based deep neural network (DNN) estimators for nonparametric regression under speckle noise.
result Established minimax rates for speckle denoising, matching those for additive Gaussian noise alone.
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.
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…
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.
In this contribution, we present a novel approach for segmenting laser radar (lidar) imagery into geometric time-height cloud locations with a fully convolutional network (FCN). We describe a semi-supervised learning method to train the FCN by: pre-training the classification layers of the FCN with image-level annotati…
We present an approach for polarimetric Synthetic Aperture Radar (SAR) image region boundary detection based on the use of B-Spline active contours and a new model for polarimetric SAR data: the GHP distribution. In order to detect the boundary of a region, initial B-Spline curves are specified, either automatically or…
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 α-stale distribution parameters. result The average of computed MLEs based on two projections is a fast and accurate estimator for amplitude distribution parameters.
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.
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…
Frequency estimation is a fundamental problem in signal processing, with applications in radar imaging, underwater acoustics, seismic imaging, and spectroscopy. The goal is to estimate the frequency of each component in a multisinusoidal signal from a finite number of noisy samples. A recent machine-learning approach u…
This paper improves land cover classification using global spatial features in CNN.
problem Limited classification accuracy and universality of traditional remote sensing image classification methods.
method Integrates global spatial features into a dual-branch CNN for hyperspectral/SAR imagery classification.
result The proposed method outperforms traditional single-channel CNN methods.
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.
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 …