Results 31 to 40 of about 1,725,722 (217)

Micro-Doppler Estimation and Analysis of Slow Moving Objects in Forward Scattering Radar System

open access: yesRemote Sensing, 2017
Micro-Doppler signature can convey information of detected targets and has been used for target recognition in many Radar systems. Nevertheless, micro-Doppler for the specific Forward Scattering Radar (FSR) system has yet to be analyzed and investigated ...
Raja Syamsul Azmir Raja Abdullah   +5 more
doaj   +1 more source

Classification of small UAVs and birds by micro-Doppler signatures [PDF]

open access: yesInternational Journal of Microwave and Wireless Technologies, 2014
The popularity of small unmanned aerial vehicles (UAVs) is increasing. Therefore, the importance of security systems able to detect and classify them is increasing as well. In this paper, we propose a new approach for UAVs classification using continuous wave radar or high pulse repetition frequency (PRF) pulse radars.
Molchanov, P.   +4 more
openaire   +3 more sources

Classification and Discrimination of Birds and Small Drones Using Radar Micro-Doppler Spectrogram Images

open access: yesSignals, 2023
This paper investigates the use of micro-Doppler spectrogram signatures of flying targets, such as drones and birds, to aid in their remote classification.
Ram M. Narayanan   +2 more
doaj   +1 more source

Omnidirectional motion classification with mono-static radar using micro-Doppler signatures [PDF]

open access: yes, 2020
In remote sensing, micro-Doppler signatures are widely used in moving target detection and automatic target recognition. However, since Doppler signatures are easily affected by the moving direction of the target, prior information of aspect angle is ...
Xiang, Wei   +5 more
core   +1 more source

Micro-motion Signature Extraction Method for Wideband Radar Based on Complex Image OMP Decomposition

open access: yesLeida xuebao, 2012
In order to extract the micro-motion signatures in condition of Migration Through Range Cells (MTRC) of micro-motional scatterers and azimuthal undersampling in wideband radar, a method based on the Orthogonal Matching Pursuit (OMP) decomposition of the
Luo Ying   +4 more
doaj   +1 more source

Classification of UAV-to-Ground Vehicles Based on Micro-Doppler Signatures Using Singular Value Decomposition and Deep Convolutional Neural Networks

open access: yesIEEE Access, 2019
Attack from the unmanned aerial vehicles (UAVs) has been the main means of high-precision strike. Therefore, classifying ground vehicles from the UAV with high accuracy is of great significance.
Lingzhi Zhu   +5 more
doaj   +1 more source

Arm Motion Classification Using Time-Series Analysis of the Spectrogram Frequency Envelopes

open access: yesRemote Sensing, 2020
Hand and arm gesture recognition using radio frequency (RF) sensing modality proves valuable in man−machine interfaces and smart environments. In this paper, we use the time-series analysis method to accurately measure the similarity of the micro ...
Zhengxin Zeng   +2 more
doaj   +1 more source

Gait Classification Based on Micro-Doppler Features [PDF]

open access: yes, 2016
This paper focuses on the classification of human gaits based on micro-Doppler signatures. The micro-Doppler signatures can represent detailed information about the human gaits, which helps in judging the threat of a personnel target. The proposed method
Li, Gang   +14 more
core   +1 more source

A 1D Cascaded Denoising and Classification Framework for Micro-Doppler-Based Radar Target Recognition

open access: yesRemote Sensing
Micro-Doppler signatures play a crucial role in capturing target features for the radar classification task, and the time–frequency distribution method is widely used to represent micro-Doppler signatures in many applications including human activities ...
Beili Ma, Baixiao Chen
doaj   +1 more source

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

open access: yesIET 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   +5 more
doaj   +1 more source

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