GPU acceleration of range alignment based on minimum entropy criterion

2013 ◽  
Author(s):  
Shi Xin-Liang ◽  
Xie Xiao-Chun
Keyword(s):  
Gpu Acceleration ◽  
Minimum Entropy ◽  
Range Alignment ◽  
Entropy Criterion

Discrete time ∞ controllers satisfying a minimum entropy criterion

1990 ◽  
Vol 14 (4) ◽  
pp. 275-286 ◽  
Author(s):  
P.A. Iglesias ◽  
D. Mustafa ◽  
K. Glover
Keyword(s):  
Discrete Time ◽  
Minimum Entropy ◽  
Entropy Criterion

Minimum entropy deconvolution with frequency‐domain constraints

Geophysics ◽  
1994 ◽  
Vol 59 (6) ◽  
pp. 938-945 ◽  
Author(s):  
Mauricio D. Sacchi ◽  
Danilo R. Velis ◽  
Alberto H. Comínguez
Keyword(s):  
Linear Programming ◽  
Field Data ◽  
Fourier Transforms ◽  
Computational Cost ◽  
Linear Operators ◽  
Minimum Entropy ◽  
Limited Data ◽  
Band Limited ◽  
Entropy Criterion ◽  
Domain Constraints

A method for reconstructing the reflectivity spectrum using the minimum entropy criterion is presented. The algorithm (FMED) described is compared with the classical minimum entropy deconvolution (MED) as well as with the linear programming (LP) and autoregressive (AR) approaches. The MED is performed by maximizing an entropy norm with respect to the coefficients of a linear operator that deconvolves the seismic trace. By comparison, the approach presented here maximizes the norm with respect to the missing frequencies of the reflectivity series spectrum. This procedure reduces to a nonlinear algorithm that is able to carry out the deconvolution of band‐limited data, avoiding the inherent limitations of linear operators. The proposed method is illustrated under a variety of synthetic examples. Field data are also used to test the algorithm. The results show that the proposed method is an effective way to process band‐limited data. The FMED and the LP arise from similar conceptions. Both methods seek an extremum of a particular norm subjected to frequency constraints. In the LP approach, the linear programming problem is solved using an adaptation of the simplex method, which is a very expensive procedure. The FMED uses only two fast Fourier transforms (FFTs) per iteration; hence, the computational cost of the inversion is reduced.

UKF Based Nonlinear Filtering Using Minimum Entropy Criterion

2013 ◽  
Vol 61 (20) ◽  
pp. 4988-4999 ◽  
Author(s):  
Yu Liu ◽  
Hong Wang ◽  
Chaohuan Hou
Keyword(s):  
Nonlinear Filtering ◽  
Minimum Entropy ◽  
Entropy Criterion

scholarly journals Improvement of Stripmap SAR Autofocus Based on Minimum-Entropy Criterion

2021 ◽  
Author(s):  
Tao Li ◽  
Yaowen Fu ◽  
Jianfeng Zhang ◽  
Wenpeng Zhang ◽  
Wei Yang
Keyword(s):  
Phase Error ◽  
Variable Step Size ◽  
Minimum Entropy ◽  
Step Size ◽  
Algorithm Performance ◽  
Variable Step ◽  
Speed Up ◽  
Autofocus Algorithm ◽  
Entropy Criterion ◽  
New Phase

Autofocus is an essential part of the SAR imaging process. Multi-subaperture autofocus algorithm is a commonly used autofocus algorithm for processing SAR stripmap mode data. The multi-subaperture autofocus algorithm has two main steps, the first is to estimate the phase error gradient within the subaperture, the second is to splice the phase error gradient, that is, to remove the shift amount between the estimated adjacent subapertures’ error gradients. Previous gradient-splicing algorithms assume that the estimation of subaperture error is accurate, but when the estimation of subaperture phase error gradients is not accurate enough, these algorithm performance will be degraded. A new phase error gradient splicing algorithm is proposed in this paper. It roughly estimates the shift amount first, and then finely estimates the shift amount based on the minimum-entropy criterion, which can improve the robustness of splicing especially when the estimation of the phase error gradients of the subaperture is not accurate enough. To speed up the algorithm, a variable-step-size search method is used. Simulation and experimental results show that the algorithm has enough accuracy and still has good performance when other splicing algorithms doesn’t perform well.

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