Citation: Dai, T.; Xu, S.; Tian, B.; Hu,
J.; Zhang, Y.; Chen, Z. Extraction of
Micro-Doppler Feature Using LMD
Algorithm Combined Supplement
Feature for UAVs and Birds
Classification. Remote Sens. 2022, 14,
2196. https://doi.org/10.3390/
rs14092196
Academic Editors: Yangquan Chen,
Subhas Mukhopadhyay, Nunzio
Cennamo, M. Jamal Deen, Junseop
Lee and Simone Morais
Received: 19 March 2022
Accepted: 28 April 2022
Published: 4 May 2022
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Article
Extraction of Micro-Doppler Feature Using LMD Algorithm
Combined Supplement Feature for UAVs
and Birds Classification
Ting Dai , Shiyou Xu, Biao Tian * , Jun Hu, Yue Zhang and Zengping Chen
School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University,
Shenzhen 518107, China; dait5@mail2.sysu.edu.cn (T.D.); xushy36@mail.sysu.edu.cn (S.X.);
hujun25@mail.sysu.edu.cn (J.H.); zhangyue8@mail.sysu.edu.cn (Y.Z.); chenzengp@mail.sysu.edu.cn (Z.C.)
* Correspondence: tianb28@mail.sysu.edu.cn
Abstract:
In the past few decades, the demand for reliable and robust systems capable of monitoring
unmanned aerial vehicles (UAVs) increased significantly due to the security threats from its wide
applications. During UAVs surveillance, birds are a typical confuser target. Therefore, discriminating
UAVs from birds is critical for successful non-cooperative UAVs surveillance. Micro-Doppler signa-
ture (m-DS) reflects the scattering characteristics of micro-motion targets and has been utilized for
many radar automatic target recognition (RATR) tasks. In this paper, the authors deploy local mean
decomposition (LMD) to separate the m-DS of the micro-motion parts from the body returns of the
UAVs and birds. After the separation, rotating parts will be obtained without the interference of the
body components, and the m-DS features can also be revealed more clearly, which is conducive to
feature extraction. What is more, there are some problems in using m-DS only for target classification.
Firstly, extracting only m-DS features makes incomplete use of information in the spectrogram.
Secondly, m-DS can be observed only for metal rotor UAVs, or large UAVs when they are closer to the
radar. Lastly, m-DS cannot be observed when the size of the birds is small, or when it is gliding. The
authors thus propose an algorithm for RATR of UAVs and interfering targets under a new system of
L band staring radar. In this algorithm, to make full use of the information in the spectrogram and
supplement the information in exceptional situations, m-DS, movement, and energy aggregation
features of the target are extracted from the spectrogram. On the benchmark dataset, the proposed
algorithm demonstrates a better performance than the state-of-the-art algorithms. More specifically,
the equal error rate (EER) proposed is 2.56% lower than the existing methods, which demonstrates
the effectiveness of the proposed algorithm.
Keywords:
micro-Doppler signature; local mean decomposition; unmanned aerial vehicles; radar
automatic target recognition; staring radar
1. Introduction
Over the past decade, the equipment cost and operational complexity of unmanned
aerial vehicles (UAVs) has been dramatically decreased while the performance has been
increased [
1
]. Thus, technological advancement fascinates a growing number of civilians.
These platforms are used not only for leisure and filming but also for agricultural appli-
cations and environmental monitoring. Nevertheless, UAVs have been used by criminals
and antisocial groups for unlawful purposes such as violating privacy or transporting
explosives. The security threat has become more prevalent both in the military and civil-
ian spheres. Hence, there is a significant demand for reliable and robust detection and
classification of UAVs.
Radar is widely used in surveillance systems since it provides fast remote sensing
capabilities regardless of weather or lighting conditions. Staring radar provides high
Doppler resolution since it enables longer coherent integration. Therefore, staring radar
Remote Sens. 2022, 14, 2196. https://doi.org/10.3390/rs14092196 https://www.mdpi.com/journal/remotesensing
