scholarly journals Signal-processing techniques to reduce the sinusoidal steady-state error in the FDTD method

2000 ◽  
Vol 48 (4) ◽  
pp. 585-593 ◽  
Author(s):  
L. Gurel ◽  
U. Oguz
Keyword(s):  
Signal Processing ◽  
Steady State ◽  
Fdtd Method ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
State Error

Signal Processing Techniques for Nonlinear Structural Dynamical Systems

1991 ◽  
Vol 44 (11S) ◽  
pp. S214-S218 ◽  
Author(s):  
C. Pezeshki ◽  
W. H. Miles ◽  
S. Elgar
Keyword(s):  
Signal Processing ◽  
Steady State ◽  
Wavelet Transforms ◽  
Galerkin Approximation ◽  
Higher Order ◽  
Steady State Response ◽  
State Response ◽  
Higher Order Spectra ◽  
Processing Techniques ◽  
Signal Processing Techniques

Various signal processing techniques are introduced into the structural dynamics literature, notably higher-order spectra for steady-state response and wavelet transforms for transient response of systems. The structural behavior of the buckled beam, modeled by the one-mode Galerkin approximation is examined to demonstrate the utility of the techniques. Higher-order spectra illuminate nonlinear energy coupling mechanisms in the frequency domain for the steady state response. Wavelet transforms show the development of the frequency spectrum in the transient portion of the response.

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

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