Télémètre à détection coherente avec un laser CO2 à ondes entretenues : étude et conception

1983 ◽  
Vol 61 (2) ◽  
pp. 318-331 ◽  
Author(s):  
Denis Vincent ◽  
Gabriel Otis
Keyword(s):  
Detection System ◽  
Signal To Noise Ratio ◽  
Optical Power ◽  
Experimental System ◽  
Intermediate Frequency ◽  
Local Oscillator ◽  
Heterodyne Detection ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Rotating Target

We performed a theoretical and experimental study of a 10.6 μm heterodyne detection system with nonlinear postdetection. A single laser serves as both transmitter and local oscillator; the intermediate frequency is given by the Doppler effect due to a rotating target. An electrooptic crystal modulates the amplitude of the laser beam at a frequency of 15 kHz; a synchronous voltmeter measures the return signal after the nonlinear element. Values of the signal-to-noise ratio with respect to incident optical power agree with the results of the theoretical model. In particular, experimentally measured target-induced frequency spreading effects on the signal-to-noise ratio correspond to the predictions of the model. We also describe an experimental system.

scholarly journals An SNR Estimation Technique Based on Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1139 ◽  
Author(s):  
Kai Yang ◽  
Zhitao Huang ◽  
Xiang Wang ◽  
Fenghua Wang
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Intermediate Frequency ◽  
Estimation Technique ◽  
Signal To Noise ◽  
Snr Estimation ◽  
Application Range ◽  
Noise Ratio ◽  
Priori Information

Signal-to-noise ratio (SNR) is a priori information necessary for many signal processing algorithms or techniques. However, there are many problems exsisting in conventional SNR estimation techniques, such as limited application range of modulation types, narrow effective estimation range of signal-to-noise ratio, and poor ability to accommodate non-zero timing offsets and frequency offsets. In this paper, an SNR estimation technique based on deep learning (DL) is proposed, which is a non-data-aid (NDA) technique. Second and forth moment (M2M4) estimator is used as a benchmark, and experimental results show that the performance and robustness of the proposed method are better, and the applied ranges of modulation types is wider. At the same time, the proposed method is not only applicable to the baseband signal and the incoherent signal, but can also estimate the SNR of the intermediate frequency signal.

A constrained minimum power adaptive beamformer with time‐varying adaptation rate

Geophysics ◽  
1979 ◽  
Vol 44 (6) ◽  
pp. 1088-1096 ◽  
Author(s):  
Wen‐Wu Shen
Keyword(s):  
Adaptive Algorithm ◽  
Detection System ◽  
Signal To Noise Ratio ◽  
High Amplitude ◽  
Research Station ◽  
Power Performance ◽  
Signal To Noise ◽  
Filter Output ◽  
Short Period ◽  
Noise Ratio

A linear adaptive algorithm was developed for array beamforming purposes. The design goal for the algorithm is to minimize the squared filter output subject to filter constraints which allow energy propagating from the array steering direction to pass without being distorted. The adaptive filter coefficients were initialized to satisfy the constraints which were preserved during the iterations. The adaptation rate is inversely varied with filter output and total input channel power. Performance of the algorithm was studied using the recorded short‐period array data from the Korean Seismic Research Station. Processed were a high‐amplitude signal from Kamchatka, a medium‐amplitude signal from eastern Kazakh, and a number of low‐amplitude signals from central Eurasia. Results of signal‐to‐noise ratio gain relative to a conventional beamformer among the events tested were consistent and were in the range of 4.5 to 6.5 dB in the wide passband. Much better signal‐to‐noise ratio improvement was obtained in the low‐frequency passband. The adaptive algorithm was programmed in the real‐time mode and can be implemented in a front‐end detection system.

Quantitative Analysis of Noise Impact on Duffing Chaotic Detection System Using Lyapunov Characteristic Exponents

2011 ◽  
Vol 130-134 ◽  
pp. 1331-1337
Author(s):  
Wen Jing Hu ◽  
Zhi Zhen Liu ◽  
Zhi Hui Li
Keyword(s):  
Detection System ◽  
Signal To Noise Ratio ◽  
Duffing Oscillator ◽  
Threshold Value ◽  
System State ◽  
Signal To Noise ◽  
Phase Trajectories ◽  
Lyapunov Characteristic Exponents ◽  
Noise Ratio ◽  
System States

Performance of the Duffing oscillator to detect weak signals buried in heavy noise is analyzed quantitatively by LCEs. First in the case of noise, differential equations to compute LCE s are derived using RHR algorithm, so the quantitative criteria to identify system states are obtained. Then using LCEs, the threshold value of the forced periodic term is found accurately. Finally the system state and state change are analyzed using LCEs by keeping the threshold value and varying the noise intensity, and the minimum signal to noise ratio is determined. By contrast of phase trajectories and LCEs, it shows that phase trajectories disturbed by strong noise sometimes are ambiguous to our eyes, but through LCEs, the system state can be identified clearly and quantitatively especially in strong noise background. So the minimum signal to noise ratio can be obtained accurately.

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