Relevant bibliographies by topics / Doppler Signals

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  1. 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. Finally, the Doppler shift of wideband signals is extracted through matching degree optimization. Simulation results show that the proposed method can extract the Doppler shift characteristics of wideband signals effectively.
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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-Doppler signals based on Hilbert–Huang transform and analytical mode decomposition (HHT-AMD) is proposed. In this method, the multicomponent micro-Doppler signals are firstly decomposed by empirical mode decomposition, and the decomposed signal components are transformed by Hilbert transform to get the Hilbert-Huang spectrum and marginal spectrum. Through the spectrum processing, we get the frequency distribution of each signal component. The next step is to judge whether there exists frequency aliasing in each signal component. If there is aliasing, the AMD method is used to decompose the signal until all the decomposed signals are mono-component signals. Evaluation considerations are covered with numerical simulations and experiments on measured radar data. The results demonstrate that compared with conventional HHT, the proposed method yields accurate decomposition for multicomponent micro-Doppler signals and improves the robustness of decomposition. The method presented here can also be applied in various settings of non-stationary signal analysis and filtering.
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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−β is the best radar for detecting micro-Doppler (i.e., JEM signals) produced by the rotating blades of a quadrotor drone, DJI Phantom 4, because the detection probability of JEM signals is almost 100%, with approximately 2 peaks, whose magnitudes are similar to that of the body Doppler. In contrast, Radar−α can barely detect any micro-Doppler, and Radar−γ detects weak micro-Doppler signals, whose magnitude is only 10% of the body Doppler’s. Proper radar dwell time is the key to micro-Doppler detection. This research provides an idea for designing a cognitive micro-Doppler radar by changing radar dwell time for detecting and tracking micro-Doppler signals of drones.
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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–frequency distribution is constructed by applying generalized S-transform to the raw signal acquired by a microphone. Based on a morphological filter, several time–frequency distributions corresponding to different single source Doppler fault signals are extracted from the original time–frequency distribution. Subsequently, the time–frequency distributions are reverted to time signals by inverse generalized S-transform. Then, a resampling time series is built by time–frequency amplitude matching to obtain the correct signals without Doppler distortion. Finally, the bearing fault is diagnosed by the envelope spectrum of the correction signal. The effectiveness of this method is verified by simulated and practical signals.
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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 original SNR of 0 dB, 5 dB, and 10 dB in both low- and high-frequency ranges are significantly improved by wavelet packet threshold denoising. A double-beam and double-scattering LDV system is built in our laboratory. For measured signals obtained from the experimental system, the minimum relative error of denoised signal is only 0.079% (using minimaxi rule). The denoised waveforms of simulated and experimental signals are much more smooth and clear than that of original signals. Generally speaking, denoising effects of minimaxi and saqtwolog rules are better than those of heursure and rigrsure rules. As shown in the processing and analysis of simulated and experimental signals, denoising methods based on wavelet packet threshold have ability to depress the noise in laser Doppler signal and improve the precision of signal processing. Owing to its effectiveness and practicability, wavelet packet threshold denoising is a practical method for LDV signal processing.
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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 between plus or minus several thousands Hz. Comparing to traditional measurement method of Doppler, the method makes it possible to fast track phase of signal in large dynamic range in synchronous technology.
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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 Doppler signals (from a Dantec fibre-based PDA system with a BSA signal processor) were recorded and used as input to the software signal processor. The signals from the three photomultipliers were input first into a Picoscope and then into the BSA processor. In this way droplet velocities and size estimates would be available from the BSA as control data. The signal outputs were taken as csv files, and input directly into the software signal processor. Initially the software determined the time location of the centre of each signal burst envelop. This approach was shown to measure signal delays from single cycle to multiple cycles. For this experiment, the software was modified by adding a zero-crossing approach to measure the single cycle delays. The introduction of this method should establish the accuracy of the complete software package in the real world as the results from the preliminary experiment show good agreement between the two techniques.
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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 speed, Doppler frequency difference (DFD), and Doppler magnitude ratio (DMR). Our findings show that the micro-Doppler signals of lifting blades in all three drone types were stronger than those of puller blades. Specifically, the DFD and DMR values of pusher blades were below 100 Hz and 0.3, respectively, which were much smaller than the 200 Hz and 0.8 values for lifting blades. The micro-Doppler signals of the puller blades were weaker and more stable than those of the lifting blades. Our study demonstrates the potential of using micro-Doppler signatures modulated by different blades for improving drone detection and the identification of drone types by drone detection radar.
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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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