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.
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169,181 papers · 148 categories
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48 results for “radar waveforms”
Develops a new model for radar waveform classification and clustering.
problem Classifying and clustering radar waveforms with different modulation types.
method Introduces a generalized multivariate Student-t mixture model with a new prior distribution for hyper-parameters.
result The method is less sensitive to initialization and provides more accurate results.
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.
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.
Bayesian method refines surrogate models for accurate full waveform inversion.
problem Complex input/output relations in full waveform inversion make accurate surrogate models difficult.
method Iterative refinement of surrogate models using MCMC samples and progressively expanding frequency bandwidth.
result Highly accurate surrogate model across full bandwidth enables accurate final MCMC inversion.
Deep Radar Detectorcs.CV
Paper introduces deep learning for radar processing.
problem Lack of labeled radar data and need for expensive calibration.
method Uses deep learning on radar complex data, trains on calibration data, introduces radar augmentation.
result Superior performance on radar 4D detection task compared to classical methods.
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.
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…
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.
Paper proposes a new loss function for training neural speech models.
problem Training high-performance neural speech waveform models.
method Uses short-time Fourier transform (STFT) spectra and assumes Gaussian and von Mises distributions for amplitude and phase spectra.
result Synthesized high-quality speech waveforms.
This research uses PointNets to detect 2D objects from radar data.
problem Detecting 2D objects from sparse radar data for automated driving.
method Adapting PointNets for radar data, performing 2D object classification and bounding box regression.
result Demonstrates the potential of PointNets for 2D object detection in radar data.
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.
Pre-training deep RF classifiers with autoencoders mitigates adversarial attacks.
problem Adversarial examples in RF deep learning cause targeted misclassifications.
method Pre-training the classifier using an autoencoder.
result Pre-training method validates as a viable mitigation against adversarial attacks.
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
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…
Paper uses FMCW radar and FCN for object detection and 3D estimation.
problem Object detection and 3D estimation using FMCW radar.
method Employed deep learning (FCN) over traditional signal processing. Normalization method applied to radar signal.
result System successfully detects and estimates 3D position of objects in noisy environments.
-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.
DiffWave generates high-fidelity audio waveforms efficiently.
problem Conditional and unconditional audio waveform generation.
method Non-autoregressive diffusion model using Markov chain synthesis.
result DiffWave produces high-quality audios in various tasks.
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.
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.
DYMAG uses dynamic waveforms to improve graph neural networks.
problem Improving graph neural networks for better graph understanding.
method DYMAG employs dynamical system-based waveforms for message aggregation in graph neural networks.
result DYMAG outperforms baseline models in graph recovery, property prediction, and random graph generation.
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.
WaveCycleGAN converts synthetic speech to natural speech using cycle-consistent adversarial networks.
problem Over-smoothing effect in synthetic speech, leading to quality degradation.
method Cycle-consistent adversarial networks for waveform-level modification.
result Improves naturalness of generated speech sounds.
This paper improves speech recognition by using raw waveform signals in multi-span CNN acoustic models.
problem Improving speech recognition accuracy using raw waveform signals.
method Proposes a novel multi-span structure for acoustic modelling based on raw waveform signals with multiple CNN input layers.
result Multi-span acoustic models yield a lower word error rate (WER) than traditional FBANK feature-based models.
This study proposes a fully convolutional network (FCN) model for raw waveform-based speech enhancement. The proposed system performs speech enhancement in an end-to-end (i.e., waveform-in and waveform-out) manner, which dif-fers from most existing denoising methods that process the magnitude spectrum (e.g., log power …
NSF models generate speech waveforms faster and better than WaveNet.
problem Efficiently generating speech waveforms for statistical parametric synthesis.
method Neural source-filter (NSF) models that combine sine-based excitation, non-AR filter, and conditional preprocessing.
result NSF models generate waveforms 100 times faster than WaveNet and have better quality.
A faster neural waveform model for speech synthesis.
problem Slow waveform generation in existing neural models.
method Proposes a non-autoregressive neural source-filter model.
result Generated waveforms 100 times faster than AR WaveNet.
Paper presents a novel waveform-to-waveform model for music source separation.
problem Isolating individual instruments from mixed music recordings.
method Adapted Conv-Tasnet to waveform domain and developed Demucs with U-Net and bidirectional LSTM.
result Demucs achieves superior performance on music source separation tasks, surpassing existing state-of-the-art.
New radar-based method improves multiclass classification of road users, especially in challenging conditions.
problem Accurate classification of multiple road users in challenging scenarios.
method 50 features extracted from radar data, subset chosen, tested on random forest and LSTM classifiers, addressed data imbalance issues.
result Substantial improvements in multiclass classification compared to ordinary methods.
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.
Artificial neural networks infer gravitational-wave parameters from reduced-order waveforms.
problem Efficiently infer gravitational-wave parameters from noisy data.
method Represent waveforms as weighted sums over reduced bases, train neural networks to map source parameters to coefficients.
result Fast and accurate interpolation of gravitational-wave coefficients.
Cognitive radar selects optimal antenna subarrays using deep learning.
problem Optimize radar antenna selection for cost and performance.
method Convolutional Neural Network (CNN) for multi-class classification.
result CNN provides 22% better classification performance and 72% more accurate DoA estimates.
Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSSeess.SP
Automated radar data labeling using GNSS improves efficiency without sacrificing accuracy.
problem Manual annotation of automotive radar data is time-consuming and error-prone.
method The article introduces an automated system using GNSS to acquire data labels.
result The proposed method provides clear advantages in time savings with minimal data distortion.
WaveCycleGAN2 improves speech synthesis quality by reducing aliasing.
problem Human ear can still distinguish synthesized speech from natural speech.
method WaveCycleGAN2 uses generators without down/up-sampling modules and combines discriminators from waveform and acoustic parameter domains.
result WaveCycleGAN2 achieves high-quality speech synthesis with comparable mean opinion scores to natural speech.
VPFD uses vocoder features for adversarial training in VC.
problem Adversarial training on waveform data is time-consuming and memory-intensive.
method VPFD employs vocoder features for adversarial training.
result VPFD achieves VC performance comparable to waveform discriminators with reduced training time and memory.
Real-time speech enhancement model removes various noises and reverb.
problem Real-time speech enhancement in noisy environments.
method Causal speech enhancement model using encoder-decoder architecture with skip-connections, optimized in time and frequency domains.
result The model matches state-of-the-art performance while working directly on raw waveform.
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.
This study improves text-to-speech synthesis using GANs for glottal excitation.
problem Slow inference and computational cost of WaveNet and difficulty in parallel training of GANs.
method Adopted GANs for parallel waveform generation in speech signal and glottal excitation.
result GAN-based glottal excitation model achieves quality and voice similarity on par with WaveNet.
Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensemblescs.LG
Radar-based classification improves accuracy of autonomous driving by identifying new classes.
problem Challenges in classifying sparse radar data for autonomous driving.
method Ensemble of classifiers using one-vs-one and one-vs-all strategies, with feature selection.
result Improved classification performance and identification of novel classes.
Cross-Representation Transferability of Adversarial Attacks: From Spectrograms to Audio Waveformscs.SD
Adversarial attacks on spectrograms can fool audio classifiers trained on waveforms.
problem Susceptibility of audio classifiers to adversarial attacks on spectrograms.
method Applying adversarial attacks to spectrograms and reconstructing audio waveforms.
result Perturbed spectrograms can fool 2D CNNs and 1D CNNs trained on audio waveforms.
Proposes a speaker-independent GlotNet vocoder using WaveNet for speech generation.
problem Lack of efficient multi-speaker WaveNet models with limited resources.
method Uses source-filter model of speech production to train a WaveNet for glottal excitation.
result Proposed GlotNet vocoder performs favorably to direct WaveNet vocoder in speech quality.
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.
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.
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.