On Target Detection by Quantum Radar (Preprint)
- URL: http://arxiv.org/abs/2403.00047v1
- Date: Thu, 29 Feb 2024 18:58:40 GMT
- Title: On Target Detection by Quantum Radar (Preprint)
- Authors: Gaspare Galati, Gabriele Pavan
- Abstract summary: Noise Radar and Quantum Radar exploit randomness of transmitted signal to enhance radar covertness and to reduce mutual interference.
Various Quantum Radar proposals cannot lead to any useful result, especially, but not limited to, the alleged detection of stealth targets.
- Score: 1.0878040851637998
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Both Noise Radar and Quantum Radar, with some alleged common features,
exploit the randomness of the transmitted signal to enhance radar covertness
and to reduce mutual interference. While Noise Radar has been prototypically
developed and successfully tested in many environments by different
organizations, the significant investments on Quantum Radar seem not to be
followed by practically operating prototypes or demonstrators. Starting from
the trivial fact that radar detection depends on the energy transmitted on the
target and backscattered by it, some detailed evaluations in this work show
that the detection performance of all the proposed QR types in the literature
are orders of magnitude below the ones of a much simpler and cheaper equivalent
classica radar set, in particular of the NR type. Moreover, the absence of a,
sometimes alleged, Quantum radar cross section different from the radar cross
section is explained. Hence, the various Quantum Radar proposals cannot lead to
any useful result, especially, but not limited to, the alleged detection of
stealth targets.
Related papers
- Long-Range Entangled Quantum Noise Radar Over Order of Kilometer [0.0]
In this paper, an explicit expression for the maximum detection range of an entangled quantum two-mode squeezed (QTMS) radar has been derived.
We show that one can view a QTMS radar as a traditional radar with a reduced threshold signal-to-noise ratio.
It is possible to implement a QTMS radar with a maximum detection range of up to 2km, which is suitable for recognizing small unmanned aerial vehicles at urban distances.
arXiv Detail & Related papers (2024-06-15T07:13:42Z) - Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar [62.51065633674272]
We introduce Radar Fields - a neural scene reconstruction method designed for active radar imagers.
Our approach unites an explicit, physics-informed sensor model with an implicit neural geometry and reflectance model to directly synthesize raw radar measurements.
We validate the effectiveness of the method across diverse outdoor scenarios, including urban scenes with dense vehicles and infrastructure.
arXiv Detail & Related papers (2024-05-07T20:44:48Z) - Multi-stage Learning for Radar Pulse Activity Segmentation [51.781832424705094]
Radio signal recognition is a crucial function in electronic warfare.
Precise identification and localisation of radar pulse activities are required by electronic warfare systems.
Deep learning-based radar pulse activity recognition methods have remained largely underexplored.
arXiv Detail & Related papers (2023-12-15T01:56:27Z) - Echoes Beyond Points: Unleashing the Power of Raw Radar Data in
Multi-modality Fusion [74.84019379368807]
We propose a novel method named EchoFusion to skip the existing radar signal processing pipeline.
Specifically, we first generate the Bird's Eye View (BEV) queries and then take corresponding spectrum features from radar to fuse with other sensors.
arXiv Detail & Related papers (2023-07-31T09:53:50Z) - Semantic Segmentation of Radar Detections using Convolutions on Point
Clouds [59.45414406974091]
We introduce a deep-learning based method to convolve radar detections into point clouds.
We adapt this algorithm to radar-specific properties through distance-dependent clustering and pre-processing of input point clouds.
Our network outperforms state-of-the-art approaches that are based on PointNet++ on the task of semantic segmentation of radar point clouds.
arXiv Detail & Related papers (2023-05-22T07:09:35Z) - Entanglement-assisted multi-aperture pulse-compression radar for angle
resolving detection [5.109700506364796]
Entanglement has been known to boost target detection, despite it being destroyed by lossy-noisy propagation.
We propose a quantum pulse-compression radar to extend entanglement's benefit to target range estimation.
arXiv Detail & Related papers (2022-07-22T05:22:50Z) - Unsupervised Domain Adaptation across FMCW Radar Configurations Using
Margin Disparity Discrepancy [17.464353263281907]
In this work, we consider the problem of unsupervised domain adaptation across radar configurations in the context of deep-learning human activity classification.
We focus on the theory-inspired technique of Margin Disparity Discrepancy, which has already been proved successful in the area of computer vision.
Our experiments extend this technique to radar data, achieving a comparable accuracy to fewshot supervised approaches for the same classification problem.
arXiv Detail & Related papers (2022-03-09T09:11:06Z) - Radar-based Materials Classification Using Deep Wavelet Scattering
Transform: A Comparison of Centimeter vs. Millimeter Wave Units [0.0]
This research considers two radar units with different frequency ranges: Walabot-3D (6.3-8 GHz) cm-wave and IMAGEVK-74 (62-69 GHz) mm-wave imaging units by Vayyar Imaging.
arXiv Detail & Related papers (2022-02-08T02:07:14Z) - Anomaly Detection in Radar Data Using PointNets [7.3600716208089825]
We present an approach based on PointNets to detect anomalous radar targets.
Our method is evaluated on a real-world dataset in urban scenarios.
arXiv Detail & Related papers (2021-09-20T10:02:24Z) - LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar
Fusion [52.59664614744447]
We present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and high definition (HD) maps.
automotive radar provides rich, complementary information, allowing for longer range vehicle detection as well as instantaneous velocity measurements.
arXiv Detail & Related papers (2020-10-02T00:13:00Z) - RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects [73.80316195652493]
We tackle the problem of exploiting Radar for perception in the context of self-driving cars.
We propose a new solution that exploits both LiDAR and Radar sensors for perception.
Our approach, dubbed RadarNet, features a voxel-based early fusion and an attention-based late fusion.
arXiv Detail & Related papers (2020-07-28T17:15:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.