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Relevant bibliographies by topics / Doppler Signals

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Journal articles

Academic literature on the topic 'Doppler Signals'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Doppler Signals"

1

Fengzhen, Zhang, Li Guijuan, Zhang Zhaohui, and Hu Chen. "Doppler shift extraction of wideband signal using spectrum scaling matching." MATEC Web of Conferences 208 (2018): 01001. http://dx.doi.org/10.1051/matecconf/201820801001.

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Doppler shift is an important feature of moving targets. It can be used to extract target velocity, distance, track and other movement parameters. According to the problem of extracting Doppler shift for wideband signals with unstable line spectrum or no line spectrum, we proposed a Doppler shift extraction method for wideband signals based on spectral scaling matching. Firstly, a spectrum reference matrix corresponding to different relative Doppler shift is generated. Then, the matching degree of Doppler signal spectrum and reference matrix is measured by linear correlation coefficient. Final

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2

Li, Wenchao, Gangyao Kuang, and Boli Xiong. "Decomposition of Multicomponent Micro-Doppler Signals Based on HHT-AMD." Applied Sciences 8, no. 10 (2018): 1801. http://dx.doi.org/10.3390/app8101801.

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Micro-Doppler signals analysis has been emerging as an important topic in target identification, and relative research has been focusing on features extraction and separation of the radar signals. As a time-frequency representation, the Hilbert-Huang transform (HHT) could extract the accurate instantaneous micro-Doppler signature from the radar signals by empirical mode decomposition and Hilbert transform. However, HHT has the shortcoming that it cannot decompose the signals with close-frequency components. To solve this problem, an innovative decomposition method for multicomponent micro-Dopp

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3

Gong, Jiangkun, Jun Yan, Deren Li, and Deyong Kong. "Detection of Micro-Doppler Signals of Drones Using Radar Systems with Different Radar Dwell Times." Drones 6, no. 9 (2022): 262. http://dx.doi.org/10.3390/drones6090262.

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Not any radar dwell time of a drone radar is suitable for detecting micro-Doppler (or jet engine modulation, JEM) produced by the rotating blades in radar signals of drones. Theoretically, any X-band drone radar system should detect micro-Doppler of blades because of the micro-Doppler effect and partial resonance effect. Yet, we analyzed radar data detected by three radar systems with different radar dwell times but similar frequency and velocity resolution, including Radar−α, Radar−β, and Radar−γ with radar dwell times of 2.7 ms, 20 ms, and 89 ms, respectively. The results indicate that Radar

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4

Zhang, Shangbin, Qingbo He, Haibin Zhang, Kesai Ouyang, and Fanrang Kong. "Signal separation and correction with multiple Doppler acoustic sources for wayside fault diagnosis of train bearings." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 14 (2016): 2664–80. http://dx.doi.org/10.1177/0954406216639342.

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The extraction of single train signal is necessary in wayside fault diagnosis because the acoustic signal acquired by a microphone is composed of multiple train bearing signals and noises. However, the Doppler distortion in the signal acquired by a microphone effectively hinders the signal separation and fault diagnosis. To address this issue, we propose a novel method based on the generalized S-transform, morphological filtering, and time–frequency amplitude matching-based resampling time series for multiple-Doppler-acoustic-source signal separation and correction. First, the original time–fr

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5

Grenier, N., F. Basseau, M. Rey, and L. LaGoarde-Segot. "Interpretation of Doppler signals." European Radiology 11, no. 8 (2001): 1295–307. http://dx.doi.org/10.1007/s003300100913.

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6

Zhang, Da, and Ranglei Liu. "Laser Doppler Signal Denoising Based on Wavelet Packet Thresholding Method." International Journal of Optics 2019 (November 14, 2019): 1–11. http://dx.doi.org/10.1155/2019/1097292.

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In laser Doppler velocimeter (LDV), calculation precision of Doppler shift is affected by noise contained in Doppler signal. In order to restrain the noise interference and improve the precision of signal processing, wavelet packet threshold denoising methods are proposed. Based on the analysis of Doppler signal, appropriate threshold function and decomposition layer number are selected. Heursure, sqtwolog, rigrsure, and minimaxi rules are adopted to get the thresholds. Processing results indicate that signal-to-noise ratio (SNR) and root mean square error (RMSE) of simulated signals with orig

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7

Dong, Shao Feng, Bao Qiang Du, and Wei Zhou. "Real-Time Measurement Method of Doppler Based on GPS Carrier Signals." Applied Mechanics and Materials 226-228 (November 2012): 2050–55. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.2050.

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According to Doppler effect of satellite on the time synchronization technology between satellite and the ground station, a real-time measurement method of Doppler is proposed based on GPS carrier signals. Using Doppler observations from GPS receiver, the method can real-timely measure Doppler frequency shift of GPS including dynamic Doppler and media Doppler whose error can be timely modified in the receiver end. Simulation results show that the frequency shift caused by dynamic Doppler, a main influencing factor in the course of transmission of time-frequency signal by GPS satellite, is betw

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8

Jedelsky, Jan, Milan Maly, Ondrej Cejpek, Graham Wigley, and James F. Meyers. "Software-based processing system for phase Doppler systems." EPJ Web of Conferences 264 (2022): 01019. http://dx.doi.org/10.1051/epjconf/202226401019.

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A Monte Carlo simulation of Phase Doppler systems has been developed. It consists of three sections, the droplet flow description, generation of the photomultiplier signals and then their processing to determine droplet velocities and the time shift between the signals from the three scattered light detection apertures. With highly realistic Doppler bursts being simulated and processed, the question arises as to whether the signal processing software could be used to process ‘real-world’ experimental signals. In a preliminary assessment of its capabilities in such a situation, actual spray Dop

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9

Yan, Jun, Huiping Hu, Jiangkun Gong, Deyong Kong, and Deren Li. "Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types." Drones 7, no. 4 (2023): 280. http://dx.doi.org/10.3390/drones7040280.

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In this study, we examine the use of micro-Doppler signals produced by different blades (i.e., puller and lifting blades) to aid in radar-based target recognition of small drones. We categorize small drones into three types based on their blade types: fixed-wing drones with only puller blades, multi-rotor drones with only lifting blades, and hybrid vertical take-off and landing (VTOL) fixed-wing drones with both lifting and puller blades. We quantify the radar signatures of the three drones using statistical measures, such as signal-to-noise ratio (SNR), signal-to-clutter ratio (SCR), Doppler

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10

Ericson, Mark A., and Lawrence L. Feth. "Detection of Doppler‐like signals." Journal of the Acoustical Society of America 103, no. 5 (1998): 3083. http://dx.doi.org/10.1121/1.422913.

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