Micro-Doppler Mini-UAV Classification Using Empirical-Mode Decomposition Features

Oh, Beom-Seok; Guo, Xin; Wan, Fangyuan; Toh, Kar-Ann; Lin, Zhiping

doi:10.1109/lgrs.2017.2781711

Cited by 89 publications

(71 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Oh et al (2018) provides a thorough comparison of the state-of-the-art UAV classification Figure 2: Weibull probability density function for different UAV targets and noise samples. methods.…”

Section: Comments and Conclusionmentioning

confidence: 99%

10th European Conference on Radar in Meteorology and Hydrology (ERAD 2018) : 1-6 July 2018, Ede-Wageningen, The Netherlands

Vos¹,

Leijnse²,

Uijlenhoet³

2018

View full text Add to dashboard Cite

Section: Comments and Conclusionmentioning

confidence: 99%

10th European Conference on Radar in Meteorology and Hydrology (ERAD 2018) : 1-6 July 2018, Ede-Wageningen, The Netherlands

Vos¹,

Leijnse²,

Uijlenhoet³

2018

View full text Add to dashboard Cite

“…In [ 80 ], a follow-up of [ 81 ], a COTS FMCW radar system is adopted for UAV classification, assuming the target is persistent in the scene. The micro-Doppler signature and 13 different features were extracted from IMFs and fed to a classifier based on total error rate minimization.…”

Section: Literature On Drone Verification and Classificationmentioning

confidence: 99%

Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review

Coluccia

Parisi

Fascista

2020

Sensors

View full text Add to dashboard Cite

Thanks to recent technological advances, a new generation of low-cost, small, unmanned aerial vehicles (UAVs) is available. Small UAVs, often called drones, are enabling unprecedented applications but, at the same time, new threats are arising linked to their possible misuse (e.g., drug smuggling, terrorist attacks, espionage). In this paper, the main challenges related to the problem of drone identification are discussed, which include detection, possible verification, and classification. An overview of the most relevant technologies is provided, which in modern surveillance systems are composed into a network of spatially-distributed sensors to ensure full coverage of the monitored area. More specifically, the main focus is on the frequency modulated continuous wave (FMCW) radar sensor, which is a key technology also due to its low cost and capability to work at relatively long distances, as well as strong robustness to illumination and weather conditions. This paper provides a review of the existing literature on the most promising approaches adopted in the different phases of the identification process, i.e., detection of the possible presence of drones, target verification, and classification.

show abstract

“…Berndt [13] extracted rotation rate harmonic and location of modulating scatterers from HRRPs, providing effective information for the classification and identification of airborne targets. Beom-Seok et al [14] extracted eight statistical and geometrical features from decomposed waveforms by empirical-mode decomposition (EMD), showing encouraging accuracy performance for mini-UAV classification. Applying these handcrafted features has achieved good results in the field of micromotion target classification.…”

Section: Introductionmentioning

confidence: 99%

Space Precession Target Classification Based on Radar High-Resolution Range Profiles

Wang

Cheng

Zhang

et al. 2019

International Journal of Antennas and Propagation

View full text Add to dashboard Cite

Precession is a common micromotion form of space targets, introducing additional micro-Doppler (m-D) modulation into the radar echo. Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. This paper presents two methods for classifying different types of space precession targets from the HRRPs. We first establish the precession model of space targets and analyze the scattering characteristics and then compute electromagnetic data of the cone target, cone-cylinder target, and cone-cylinder-flare target. Experimental results demonstrate that the support vector machine (SVM) using histograms of oriented gradient (HOG) features achieves a good result, whereas the deep convolutional neural network (DCNN) obtains a higher classification accuracy. DCNN combines the feature extractor and the classifier itself to automatically mine the high-level signatures of HRRPs through a training process. Besides, the efficiency of the two classification processes are compared using the same dataset.

show abstract

Micro-Doppler Mini-UAV Classification Using Empirical-Mode Decomposition Features

Cited by 89 publications

References 17 publications

10th European Conference on Radar in Meteorology and Hydrology (ERAD 2018) : 1-6 July 2018, Ede-Wageningen, The Netherlands

10th European Conference on Radar in Meteorology and Hydrology (ERAD 2018) : 1-6 July 2018, Ede-Wageningen, The Netherlands

Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review

Space Precession Target Classification Based on Radar High-Resolution Range Profiles

Contact Info

Product

Resources

About