Related papers: A Novel Radar Constant False Alarm Rate Detection …
Compared with the sparse recovery process in traditional compressed sensing (CS) radar detector CAMP, vector AMP deep unfolding (VAMP-DU) can achieve sparse recovery over a broader range of observation matrices, with faster convergence…
Target detection is a fundamental task in radar sensing, serving as the precursor to any further processing for various applications. Numerous detection algorithms have been proposed. Classical methods based on signal processing, e.g., the…
Four-dimensional (4D) Radar is a useful sensor for 3D object detection and the relative radial speed estimation of surrounding objects under various weather conditions. However, since Radar measurements are corrupted with invalid components…
Peak detection is a fundamental task in radar and has therefore been studied extensively in radar literature. However, Integrated Sensing and Communication (ISAC) systems for sixth generation (6G) cellular networks need to perform peak…
This paper presents a new detector for filtering noise from true detections in radar data, which improves the state of the art in radar odometry. Scanning Frequency-Modulated Continuous Wave (FMCW) radars can be useful for localization and…
In this work, we develop centralized and decentralized signal fusion techniques for constant false alarm rate (CFAR) multi-target detection with a cognitive radar network in unknown noise and clutter distributions. Further, we first develop…
Classical radar detection techniques rely on adaptive detectors that estimate the noise covariance matrix from target-free secondary data. While effective in Gaussian environments, these methods degrade in the presence of clutter, which is…
The fast deployment of cognitive radar to counter jamming remains a critical challenge in modern warfare, where more efficient deployment leads to quicker detection of targets. Existing methods are primarily based on evolutionary…
Target detection is the front-end stage in any automatic target recognition system for synthetic aperture radar (SAR) imagery (SAR-ATR). The efficacy of the detector directly impacts the succeeding stages in the SAR-ATR processing chain.…
Fall detection, particularly critical for high-risk demographics like the elderly, is a key public health concern where timely detection can greatly minimize harm. With the advancements in radio frequency technology, radar has emerged as a…
In this paper, we address the limitations of traditional constant false alarm rate (CFAR) target detectors in automotive radars, particularly in complex urban environments with multiple objects that appear as extended targets. We propose a…
This paper presents a novel radar signal detection pipeline focused on detecting large targets such as cars and SUVs. Traditional methods, such as Ordered-Statistic Constant False Alarm Rate (OS-CFAR), commonly used in automotive radar, are…
Frequency agile radar (FAR) is known to have excellent electronic counter-countermeasures (ECCM) performance and the potential to realize spectrum sharing in dense electromagnetic environments. Many compressed sensing (CS) based algorithms…
Millimeter-wave (mmWave) OFDM radar equipped with rainbow beamforming, enabled by phase-time arrays (PTAs), provides wide-angle coverage and is well-suited for fast real-time target detection and tracking. However, accurate detection of…
The paper addresses the design of adaptive radar detectors having desired behavior, in Gaussian disturbance with unknown statistics. Specifically, given detection probability specifications for chosen signal-to-noise ratios and steering…
This paper introduces a method based on a deep neural network (DNN) that is perfectly capable of processing radar data from extremely thinned radar apertures. The proposed DNN processing can provide both aliasing-free radar imaging and…
Exploring the idea of phase retrieval has been intriguing researchers for decades, due to its appearance in a wide range of applications. The task of a phase retrieval algorithm is typically to recover a signal from linear phaseless…
In this paper, we consider a general form of noisy compressive sensing (CS) where the sensing matrix is not precisely known. Such cases exist when there are imperfections or unknown calibration parameters during the measurement process.…
Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse problem, where one seeks to recover a sparse signal from a few…
Tensor CANDECOMP/PARAFAC decomposition (CPD) is a fundamental model for tensor reconstruction. Although the Bayesian framework allows for principled uncertainty quantification and automatic hyperparameter learning, existing methods do not…