Results 11 to 20 of about 235,258 (299)

HF Radar Signatures and Their Use for Target Classification, Recognition and Identification [PDF]

Sensors
The challenging goal of equipping HF radars with a target classification ability has been pursued for many years, yet no satisfactory system-level methodology has been reported.
Stuart Anderson
doaj   +2 more sources

High Fidelity Physics Simulation-Based Convolutional Neural Network for Automotive Radar Target Classification Using Micro-Doppler

IEEE Access, 2021
Detection and classification of vulnerable road users (VRUs) such as pedestrians and cyclists is a key requirement for the realization of fully autonomous vehicles. Radar-based classification of VRUs can be achieved by exploiting differences in the micro-
U. Chipengo, A. Sligar, S. Canta, M. Goldgruber, Hen Leibovich, S. Carpenter   +5 more
semanticscholar   +1 more source

A Grid-Based Gradient Descent Extended Target Clustering Method and Ship Target Inverse Synthetic Aperture Radar Imaging for UHF Radar

Remote Sensing, 2023
Inland shipping is of great significance in economic development, and ship surveillance and classification are of great importance for ship management and dispatch. For river ship detection, ultrahigh-frequency (UHF) radar is an effective equipment owing
Lizun Zhang, Hao Zhou, Liyun Bai, Yingwei Tian   +3 more
doaj   +1 more source

Aerial Radar Target Classification using Artificial Neural Networks

International Conference on Pattern Recognition Applications and Methods, 2020
: In this paper, we propose a new algorithm for classification of aerial radar targets by using Radar Cross Section (RCS) time-series corresponding to target detections of a given track.
Guy Ardon, Or Simko, A. Novoselsky
semanticscholar   +1 more source

Machine learning‐based approach for maritime target classification and anomaly detection using millimetre wave radar Doppler signatures

IET Radar, Sonar & Navigation, 2023
The authors present multiple machine learning‐based methods for distinguishing maritime targets from sea clutter. The main goal for this classification framework is to aid future millimetre wave radar system design for marine autonomy.
S. Rahman, A. Vattulainen, D. A. Robertson   +2 more
semanticscholar   +1 more source

Low-resolution Airborne Radar Aircraft Target Classification

Leida xuebao, 2014
Target classification is particularly important in modern and future airborne radar. Nowadays, most investigations of radar target classification are based on wideband radar signals, which have higher requirements for SNR and radar systems, and are ...
Wang Fu-you, Luo Ding, Liu Hong-wei
doaj   +1 more source

General Linear Chirplet Transform and Radar Target Classification [PDF]

AUT Journal of Electrical Engineering, 2019
In this paper, we design an attractive algorithm aiming to classify moving targets including human, animal, vehicle and drone, at ground surveillance radar systems.
Reza Amiri, Ali Shahzadi
doaj   +1 more source

Target Classification Using Frontal Images Measured by 77 GHz FMCW Radar through DCNN

Applied Sciences, 2022
This paper proposes a target classification method using radar frontal imaging measured by millimeter-wave multiple-input multiple-output (MW-MIMO) radar through deep convolutional neural networks.
Mohamed Elbeialy, Sungjin You, Byung Jang Jeong, Youngwook Kim   +3 more
doaj   +1 more source

A Machine Learning Based Vehicle Classification in Forward Scattering Radar

IEEE Access, 2022
The Forward scattering radars (FSRs) are special types of Bistatic radars in which detected targets should exist in the narrow baseline to obtain their tracking at an angle of 180 degree.
Mohammed E. A. Kanona, Mohamad Y. Alias, Mohamed Khalafalla Hassan, Khalid S. Mohamed, Mutaz H. H. Khairi, Mosab Hamdan, Yassin A. Hamdalla, Omnia M. Osman, Ahmed M. O. Ahmed   +8 more
doaj   +1 more source

Using Polarization-Diverse Wave-Coefficients for Aerospace Target Recognition

IEEE Access, 2020
Wave-coefficients (WCs) based target recognition can be theoretically applied to any a frequency range, with increased sensitivity to aspect angle at higher frequencies.
Xiaomin Jiang, Yingxin Lai, Shanjin Wang, Yue Song   +3 more
doaj   +1 more source