Two-dimensional, frequency domain, adaptive system modeling using three-dimensional spatiotemporal inputs

1995 ◽  
Vol 42 (5) ◽  
pp. 317-325 ◽  
Author(s):  
W.B. Mikhael ◽  
Haoping Yu
Keyword(s):  
Frequency Domain ◽  
Adaptive System ◽  
System Modeling ◽  
Three Dimensional ◽  
Two Dimensional

ADAPTIVE, FREQUENCY DOMAIN, 2-D MODELING USING SPATIOTEMPORAL SIGNALS

1996 ◽  
Vol 06 (04) ◽  
pp. 351-358
Author(s):  
WASFY B. MIKHAEL ◽  
HAOPING YU
Keyword(s):  
Frequency Domain ◽  
Moving Average ◽  
System Modeling ◽  
Arma Model ◽  
Model Parameters ◽  
Two Dimensional ◽  
Convergence Factor ◽  
Arma Process ◽  
Auto Regressive ◽  
Unknown System

In this paper, an adaptive, frequency domain, steepest descent algorithm for two-dimensional (2-D) system modeling is presented. Based on the equation error model, the algorithm, which characterizes the 2-D spatially linear and invariant unknown system by a 2-D auto-regressive, moving-average (ARMA) process, is derived and implemented in the 3-D spatiotemporal domain. At each iteration, corresponding to a given pair of input and output 2-D signals, the algorithm is formulated to minimize the error-function’s energy in the frequency domain by adjusting the 2-D ARMA model parameters. A signal dependent, optimal convergence factor, referred to as the homogeneous convergence factor, is developed. It is the same for all the coefficients but is updated once per iteration. The resulting algorithm is called the Two-Dimensional, Frequency Domain, with Homogeneous µ*, Adaptive Algorithm (2D-FD-HAA). In addition, the algorithm is implemented using the 2-D Fast Fourier Transform (FFT) to enhance the computational efficiency. Computer simulations demonstrate the algorithm’s excellent adaptation accuracy and convergence speed. For illustration, the proposed algorithm is successfully applied to modeling a time varying 2-D system.

FREQUENCY DOMAIN RECONSTRUCTION FOR PHOTO- AND THERMOACOUSTIC TOMOGRAPHY WITH LINE DETECTORS

2009 ◽  
Vol 19 (02) ◽  
pp. 283-306 ◽  
Author(s):  
MARKUS HALTMEIER
Keyword(s):  
Wave Equation ◽  
Frequency Domain ◽  
Boundary Data ◽  
Three Dimensional ◽  
Photoacoustic Tomography ◽  
Two Dimensional ◽  
Reconstruction Problem ◽  
Acoustic Data ◽  
Dimensional Wave ◽  
Frequency Domain Reconstruction

This paper is concerned with a version of photoacoustic tomography, that uses line shaped detectors (instead of point-like ones) for the recording of acoustic data. The three-dimensional image reconstruction problem is reduced to a series of two-dimensional ones. First, the initial data of the two-dimensional wave equation is recovered from boundary data, and second, the classical two-dimensional Radon transform is inverted. We discuss uniqueness and stability of reconstruction, and compare frequency domain reconstruction formulas for various geometries.

scholarly journals Three-dimensional terrain elevation in airborne interferometric frequency-domain radar

2017 ◽  
pp. 4-9
Author(s):  
Borys Fedotov ◽  
Sergey Stankevich ◽  
Yevhen Tsvietkov
Keyword(s):  
Image Restoration ◽  
Frequency Domain ◽  
Numerical Algorithm ◽  
Horizontal Plane ◽  
Three Dimensional ◽  
Radar Image ◽  
Two Dimensional ◽  
Terrain Elevation ◽  
Performance Calculation

This paper is devoted to the method for a three-dimensional radar image restoration of terrain elevations using airborne two antenna interferometric frequency-domain radar. A method’s main feature is the parallel obtaining of two-dimensional frequencydomain spectra both of radar terrain echo and its derivative for next synthesizing. The architecture of such interferometric radar is proposed, and math equations for one’s performance calculation are presented. The numerical algorithm for the terrain elevations calculation over an arbitrary horizontal plane is developed.

Generalized two-dimensional frequency domain least square algorithm for ARMA system modeling

1999 ◽  
Vol 20 (6-7) ◽  
pp. 474 ◽  
Author(s):  
Qingwen Zhang ◽  
Jaime R. Roman ◽  
Dennis W. Davis ◽  
Wasfy B. Mikhael
Keyword(s):  
Frequency Domain ◽  
System Modeling ◽  
Least Square ◽  
Two Dimensional
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