Laboratory validation of heterodyne laser radar signal-to-noise expressions for intensity projection generation and image reconstruction

1995 ◽  
Author(s):  
Charles L. Matson ◽  
James K. Boger
Keyword(s):  
Image Reconstruction ◽  
Laser Radar ◽  
Radar Signal ◽  
Signal To Noise

Bistatic coherent laser radar signal-to-noise ratio

2002 ◽  
Vol 41 (9) ◽  
pp. 1768 ◽  
Author(s):  
Eric P. Magee ◽  
Timothy J. Kane
Keyword(s):  
Signal To Noise Ratio ◽  
Laser Radar ◽  
Radar Signal ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Coherent Laser Radar

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 Influences of weather phenomena on automotive laser radar systems

2011 ◽  
Vol 9 ◽  
pp. 49-60 ◽  
Author(s):  
R. H. Rasshofer ◽  
M. Spies ◽  
H. Spies
Keyword(s):  
High Performance ◽  
Signal Transmission ◽  
Optical Signal ◽  
Laser Radar ◽  
Radar Signal ◽  
Radar Systems ◽  
Perception System ◽  
Theoretical Investigations ◽  
Physical Principles ◽  
Transmission Models

Abstract. Laser radar (lidar) sensors provide outstanding angular resolution along with highly accurate range measurements and thus they were proposed as a part of a high performance perception system for advanced driver assistant functions. Based on optical signal transmission and reception, laser radar systems are influenced by weather phenomena. This work provides an overview on the different physical principles responsible for laser radar signal disturbance and theoretical investigations for estimation of their influence. Finally, the transmission models are applied for signal generation in a newly developed laser radar target simulator providing – to our knowledge – worldwide first HIL test capability for automotive laser radar systems.

MULTI-OBJECTIVE GA-OPTIMIZED INTERPOLATION KERNELS FOR RECONSTRUCTION OF HIGH RESOLUTION EMR IMAGES FROM LOW-SAMPLED K-SPACE DATA

2009 ◽  
Vol 08 (02) ◽  
pp. 127-140 ◽  
Author(s):  
D. BALASUBRAMANIAN ◽  
MURALI C. KRISHNA ◽  
R. MURUGESAN
Keyword(s):  
Image Reconstruction ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Computing Time ◽  
Reference Image ◽  
Signal To Noise ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Noise Ratio ◽  
Space Data

The low-frequency instrumentation and imaging capabilities facilitate electron magnetic resonance imaging (EMRI) as an emerging non-invasive imaging technology for mapping free radicals in biological systems. Unlike MRI, EMRI is implemented as a pure phase–phase encoding technique. The fast bio-clearance of the imaging agent and the requirement to reduce radio frequency power deposition dictate collection of reduced k-space samples, compromising the quality and resolution of the EMR images. The present work evaluates various interpolation kernels to generate larger k-space samples for image reconstruction, from the acquired reduced k-space samples. Using k-space EMR data sets, acquired for phantom as well as live mice, the proposed technique is critically evaluated by computing quality metrics viz. signal-to-noise ratio (SNR), standard deviation error (SDE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR) and Lui's error function (F(I)). The quantitative evaluation of 24 different interpolation functions (including piecewise polynomial functions and many windowed sinc functions) to upsample the k-space data for the Fourier EMR image reconstruction shows that at the expense of a slight increase in computing time, the reconstructed images from upsampled data, produced using Spline-sinc, Welch-sinc, and Gaussian-sinc kernels, are closer to reference image with minimal distortion. Support of the interpolating kernel is a characteristic parameter deciding the quality of the reconstructed image and the time complexity. In this paper, a method to optimize the kernel support using genetic algorithm (GA) is also explored. Maximization of the fitness function has two conflicting objectives and it is approached as a multi-objective optimization problem.

Tomographic Image Reconstruction From Laser Radar Reflective Projections

1989 ◽  
Author(s):  
R. M. Marino ◽  
R. N. Capes ◽  
W. E. Keicher ◽  
S. R. Kulkarni ◽  
J. K. Parker ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Laser Radar ◽  
Tomographic Image ◽  
Tomographic Image Reconstruction

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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