scholarly journals Recognition of flying small target based on enhanced quadratic time-frequency analysis

2021 ◽  
Vol 2132 (1) ◽  
pp. 012021
Author(s):  
Jia Guo ◽  
Xiaohong Huang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Recognition Rate ◽  
Principal Component ◽  
Single Mode ◽  
Millimeter Wave Radar ◽  
Time Frequency ◽  
Window Functions ◽  
Wave Radar ◽  
Micro Motion ◽  
Small Targets

Abstract UAVs (Unmanned Aerial Vehicles, UAVs) are flying targets that sail at low altitudes, are slower and smaller in size. Nowadays, the task of detecting and distinguishing flying small targets is very difficult, so how to efficiently recognize flying small targets in real time is a key issue of current research. In order to solve this problem, this paper proposes a method of using pseudo-WVD and image fusion to represent the characteristics of UAVs. First, the SMMWR (Single-mode millimeter wave radar, SMMWR) equipment is used to collect the echo signals of various types of UAVs, and at the same time, the two-dimensional FFT is used to extract the target micro-motion signals in the distance dimension. Secondly, PWVD is used to generate time-frequency graphs of different window functions. Finally, the images fused based on principal component analysis are sent to AlexNet for training. The result proves that the accuracy of recognition rate based on AlexNet can be 93.75%.

scholarly journals Time-Frequency Spectral Signature of Limb Movements and Height Estimation Using Micro-Doppler Millimeter-Wave Radar

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4660
Author(s):  
Yael Balal ◽  
Nezah Balal ◽  
Yair Richter ◽  
Yosef Pinhasi
Keyword(s):  
Millimeter Wave ◽  
Continuous Wave ◽  
Animal Movement ◽  
Doppler Frequency ◽  
Spectral Signature ◽  
Millimeter Wave Radar ◽  
Time Frequency ◽  
Structure And Motion ◽  
W Band ◽  
Wave Radar

We present a technique for the identification of human and animal movement and height using a low power millimeter-wave radar. The detection was based on the transmission of a continuous wave and heterodyning the received signal reflected from the target to obtain micro-Doppler shifts associated with the target structure and motion. The algorithm enabled the extraction of target signatures from typical gestures and differentiated between humans, animals, and other ‘still’ objects. Analytical expressions were derived using a pendulum model to characterize the micro-Doppler frequency shifts due to the periodic motion of the human and animal limbs. The algorithm was demonstrated using millimeter-wave radar operating in the W-band. We employed a time–frequency distribution to analyze the detected signal and classify the type of targets.

scholarly journals Biometric Recognition of Finger Knuckle Print Based on the Fusion of Global Features and Local Features

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Wenwen Li
Keyword(s):  
Recognition Rate ◽  
Principal Component ◽  
Texture Features ◽  
Single Mode ◽  
Recognition System ◽  
Local Features ◽  
Identification System ◽  
Biometric Recognition ◽  
Global Features ◽  
Hand Shape

Compared with the most traditional fingerprint identification, knuckle print and hand shape are more stable, not easy to abrase, forge, and pilfer; in aspect of image acquisition, the requirement of acquisition equipment and environment are not high; and the noncontact acquisition method also greatly improves the users’ satisfaction; therefore, finger knuckle print and hand shape of single-mode identification system have attracted extensive attention both at home and abroad. A large number of studies show that multibiometric fusion can greatly improve the recognition rate, antiattack, and robustness of the biometric recognition system. A method combining global features and local features was designed for the recognition of finger knuckle print images. On the one hand, principal component analysis (PCA) was used as the global feature for rapid recognition. On the other hand, the local binary pattern (LBP) operator was taken as the local feature in order to extract the texture features that can reflect details. A two-layer serial fusion strategy is proposed in the combination of global and local features. Firstly, the sample library scope was narrowed according to the global matching result. Secondly, the matching result was further determined by fine matching. By combining the fast speed of global coarse matching and the high accuracy of local refined matching, the designed method can improve the recognition rate and the recognition speed.

scholarly journals Detection and identification technology of rotor unmanned aerial vehicles in 5G scene

2019 ◽  
Vol 15 (6) ◽  
pp. 155014771985399 ◽  
Author(s):  
Fengtong Xu ◽  
Tao Hong ◽  
Jingcheng Zhao ◽  
Tao Yang
Keyword(s):  
Internet Of Things ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Millimeter Wave ◽  
Millimeter Wave Radar ◽  
Aerial Vehicles ◽  
Wave Radar ◽  
Radar Echo ◽  
Aerial Vehicle ◽  
Modulation Signal

In the 5G era, integration between different networks is required to realize a new world of Internet of things, the most typical model is Space–Air–Ground Internet of things. In the Space–Air–Ground Internet of things, unmanned aerial vehicle network is widely used as the representative of air-based networks. Therefore, a lot of unmanned aerial vehicle “black flying” incidents have occurred. UAVs are a kind of “low, slow and small” artificial targets, which face enormous challenges in detecting, identifying, and managing them. In order to identify the “black flying” unmanned aerial vehicle, combined with the advantages of 5G millimeter wave radar and machine learning methods, the following methods are adopted in this article. For a one-rotor unmanned aerial vehicle, the radar echo data are a single-component sinusoidal frequency modulation signal. The echo signal is conjugated first and then is subjected to a short-time Fourier transform, while the micro-Doppler has a double effect. For a multi-rotor unmanned aerial vehicle, the radar echo data are a multi-component sinusoidal frequency modulation signal, the k-order Bessel function base and the signal are used for integral projection processing, which better identifies the micro-Doppler characteristics such as the number of rotors or the rotational speed of each rotor. The noise interference is added to verify that the algorithm has better robustness. The micro-Doppler characteristics of rotor unmanned aerial vehicles are extracted by the above algorithm, and the data sets are built to train the model. Finally, the classification of unmanned aerial vehicle is realized, and the classification results are given. The research in this article provides an effective solution to solve the problem of detecting and identifying unmanned aerial vehicle by 5G millimeter wave radar in the Internet of Things, which has high practical application value.

The Large Millimeter Wave Radar Phased Antenna Array

2000 ◽  
Vol 54 (10) ◽  
pp. 101-111
Author(s):  
Aleksey Alekseevich Tolkachev ◽  
Vasiliy Andreevich Makota ◽  
Mariya Petrovna Pavlova ◽  
Anatoliy Moiseevich Nikolaev ◽  
Vladimir Victorovich Denisenko ◽  
...  
Keyword(s):  
Antenna Array ◽  
Millimeter Wave ◽  
Millimeter Wave Radar ◽  
Wave Radar ◽  
Phased Antenna Array

Antenna for Navigational Shipborne Millimeter-Wave Radar

2006 ◽  
Vol 65 (16) ◽  
pp. 1453-1462
Author(s):  
A. N. Nechiporenko ◽  
L. D. Fesenko
Keyword(s):  
Millimeter Wave ◽  
Millimeter Wave Radar ◽  
Wave Radar

scholarly journals Robust Principal Component Analysis-Based Infrared Small Target Detection

2019 ◽  
Vol 33 ◽  
pp. 9925-9926
Author(s):  
Qiwei Chen ◽  
Cheng Wu ◽  
Yiming Wang
Keyword(s):  
Iteration Process ◽  
Principal Component ◽  
Component Analysis ◽  
Low Rank ◽  
Small Target ◽  
Frame Sequence ◽  
Augmented Lagrange Multiplier ◽  
Small Targets ◽  
Infrared Small Target ◽  
Augmented Lagrange Multiplier Method

A method based on Robust Principle Component Analysis (RPCA) technique is proposed to detect small targets in infrared images. Using the low rank characteristic of background and the sparse characteristic of target, the observed image is regarded as the sum of a low-rank background matrix and a sparse outlier matrix, and then the decomposition is solved by the RPCA. The infrared small target is extracted from the single-frame image or multi-frame sequence. In order to get more efficient algorithm, the iteration process in the augmented Lagrange multiplier method is improved. The simulation results show that the method can detect out the small target precisely and efficiently.

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