Results 1 to 10 of about 17,355 (154)

Automatic recognition of sonar targets using feature selection in micro-Doppler signature

Defence Technology, 2023
Currently, the use of intelligent systems for the automatic recognition of targets in the fields of defence and military has increased significantly.
Abbas Saffari, Seyed-Hamid Zahiri, Mohammad Khishe   +2 more
doaj   +3 more sources

Analysis of Radar Doppler Signature from Human Data [PDF]

Radioengineering, 2014
This paper presents the results of time (autocorrelation) and time-frequency (spectrogram) analyses of radar signals returned from the moving human targets.
M. ANDRIĆ, D. BUJAKOVIĆ, B. BONDZULIĆ, S. SIMIĆ, B. ZRNIĆ   +4 more
doaj   +2 more sources

Radar Micro‐Doppler Signature Generation Based on Time‐Domain Digital Coding Metasurface

Advanced Science
Micro‐Doppler effect is a vital feature of a target that reflects its oscillatory motions apart from bulk motion and provides an important evidence for target recognition with radars.
Si Ran Wang, Jun Yan Dai, Jun Chen Ke, Zhan Ye Chen, Qun Yan Zhou, Zhen Jie Qi, Ying Juan Lu, Yan Huang, Meng Ke Sun, Qiang Cheng, Tie Jun Cui   +10 more
doaj   +2 more sources

Micro-Doppler Signature Analysis for Space Domain Awareness Using VHF Radar

Remote Sensing
The large quantity of resident space objects orbiting Earth poses a threat to safety and efficient operations in space. Radar sensors are well suited to detecting objects in space including decommissioned satellites and debris, whereas the more commonly ...
Emma Heading, Si Tran Nguyen, David Holdsworth, Iain M. Reid   +3 more
doaj   +3 more sources

Micro-Doppler Parameters Extraction of Precession Cone-Shaped Targets Based on Rotating Antenna

Remote Sensing, 2022
Micro-Doppler is regarded as a unique signature of a target with micro-motions. The sophisticated recognition of the cone-shaped targets can be realized through the micro-Doppler effect.
Zhihao Wang, Ying Luo, Kaiming Li, Hang Yuan, Qun Zhang   +4 more
doaj   +1 more source

Realistic Simulation of Drone Micro-Doppler Signatures

2021 18th European Radar Conference (EuRAD), 2022
This paper presents a novel approach to simulating micro-Doppler signatures caused by drones. The focus of this work is to produce realistic signatures that represent the variation that is observed in live radar measurements. In order to accomplish this, the kinematics and dynamics of a drone flight are modelled to capture the changing rotor rotation ...
Bennett, Cameron, Harman, Stephen, Petrunin, Ivan   +2 more
openaire   +1 more source

Human and bird detection and classification based on Doppler radar spectrograms and vision images using convolutional neural networks

International Journal of Advanced Robotic Systems, 2021
The study investigated object detection and classification based on both Doppler radar spectrograms and vision images using two deep convolutional neural networks.
Jnana Sai Abhishek Varma Gokaraju, Weon Keun Song, Min-Ho Ka, Somyot Kaitwanidvilai   +3 more
doaj   +1 more source

Dual-Scale Doppler Attention for Human Identification

Sensors, 2022
This paper considers a Deep Convolutional Neural Network (DCNN) with an attention mechanism referred to as Dual-Scale Doppler Attention (DSDA) for human identification given a micro-Doppler (MD) signature induced as input.
Sunjae Yoon, Dahyun Kim, Ji Woo Hong, Junyeong Kim, Chang D. Yoo   +4 more
doaj   +1 more source

Using SVM Classifier and Micro-Doppler Signature for Automatic Recognition of Sonar Targets [PDF]

Archives of Acoustics, 2023
In this paper, we propose using a propeller modulation on the transmitted signal (called sonar micro- Doppler) and different support vector machine (SVM) kernels for automatic recognition of moving sonar targets.
Abbas Saffari, Seyed Hamid Zahiri, Navid Khozein Ghanad   +2 more
doaj   +1 more source

Attention‐enhanced Alexnet for improved radar micro‐Doppler signature classification

IET Radar, Sonar & Navigation, 2023
This work introduces an attention mechanism that can be integrated into any standard convolution neural network to improve model sensitivity and prediction accuracy with minimal computational overhead.
Shelly Vishwakarma, Wenda Li, Chong Tang, Karl Woodbridge, Ravi Raj Adve, Kevin Chetty   +5 more
doaj   +1 more source