Carrier-to-noise ratio gain factor of coherent array detection system for laser radar and communications under different conditions of atmospheric turbulence

1999 ◽  
Author(s):  
Jing Xu ◽  
Larry C. Andrews ◽  
Ronald L. Phillips
Keyword(s):  
Atmospheric Turbulence ◽  
Detection System ◽  
Gain Factor ◽  
Laser Radar ◽  
Array Detection ◽  
Noise Ratio

Improvement of signal-to-noise ratio in chaotic laser radar based on algorithm implementation

2012 ◽  
Vol 10 (5) ◽  
pp. 052801-52804 ◽  
Author(s):  
Bingjie Wang Bingjie Wang ◽  
Tong Zhao Tong Zhao ◽  
Huakui Wang Huakui Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Laser Radar ◽  
Signal To Noise ◽  
Chaotic Laser ◽  
Noise Ratio ◽  
Algorithm Implementation

scholarly journals Detection of a Semi-Rough Target in Turbulent Atmosphere by an Electromagnetic Gaussian Schell-Model Beam

2019 ◽  
Vol 9 (14) ◽  
pp. 2790
Author(s):  
Xiaofei Li ◽  
Yuefeng Zhao ◽  
Xianlong Liu ◽  
Yangjian Cai
Keyword(s):  
Inverse Problem ◽  
Atmospheric Turbulence ◽  
Turbulent Atmosphere ◽  
Incident Beam ◽  
Radar System ◽  
Equivalent Model ◽  
Laser Radar ◽  
Beam Radius ◽  
Tensor Method ◽  
Set Up

The interaction of an electromagnetic Gaussian Schell-model beam with a semi-rough target located in atmospheric turbulence was studied by means of a tensor method, and the corresponding inverse problem was analyzed. The equivalent model was set up on the basis of a bistatic laser radar system and a rough target located in a turbulent atmosphere. Through mathematical deduction, we obtained detailed information about the parameters of the semi-rough target by measuring the beam radius, coherence radius of the incident beam and the polarization properties of the returned beam.

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.

Novel design for suspension array detection system

2005 ◽  
Author(s):  
Guoxiong Xu ◽  
Yan Shi ◽  
Xuxiang Ni ◽  
Zukang Lu
Keyword(s):  
Detection System ◽  
Suspension Array ◽  
Array Detection ◽  
Novel Design

Signal-to-noise-ratio equations for a heterodyne laser radar

1992 ◽  
Vol 31 (21) ◽  
pp. 4240 ◽  
Author(s):  
Charles A. DiMarzio ◽  
Scott C. Lindberg
Keyword(s):  
Signal To Noise Ratio ◽  
Laser Radar ◽  
Signal To Noise ◽  
Noise Ratio
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