A signal processing adaptive algorithm for nonstationary power signal parameter estimation

2012 ◽  
Vol 27 (3) ◽  
pp. 166-181 ◽  
Author(s):  
Shazia Hasan ◽  
P.K. Dash ◽  
S. Nanda
Keyword(s):  
Signal Processing ◽  
Parameter Estimation ◽  
Adaptive Algorithm ◽  
Signal Parameter

SFM signal parameter estimation algorithm based on Carson rule

2010 ◽  
Vol 24 (4) ◽  
pp. 353-358 ◽  
Author(s):  
Hui Xiong ◽  
Yuan Lv ◽  
Deguo Zeng ◽  
Bin Tang
Keyword(s):  
Parameter Estimation ◽  
Estimation Algorithm ◽  
Signal Parameter ◽  
Parameter Estimation Algorithm

The Cramér–Rao Bound for Signal Parameter Estimation From Quantized Data [Lecture Notes]

2022 ◽  
Vol 39 (1) ◽  
pp. 118-125
Author(s):  
Petre Stoica ◽  
Xiaolei Shang ◽  
Yuanbo Cheng
Keyword(s):  
Parameter Estimation ◽  
Signal Parameter ◽  
Lecture Notes ◽  
Cramer Rao Bound

A subspace rotation approach to signal parameter estimation

1986 ◽  
Vol 74 (7) ◽  
pp. 1044-1046 ◽  
Author(s):  
A. Paulraj ◽  
R. Roy ◽  
T. Kailath
Keyword(s):  
Parameter Estimation ◽  
Signal Parameter

Experimental Robot Identification: Advantages of Combining Internal and External Measurements and of Using Periodic Excitation

2001 ◽  
Vol 123 (4) ◽  
pp. 630-636 ◽  
Author(s):  
Walter Verdonck ◽  
Jan Swevers ◽  
Jean-Claude Samin
Keyword(s):  
Signal Processing ◽  
Parameter Estimation ◽  
Effective Means ◽  
Base Plate ◽  
Experimental Results ◽  
Parameter Estimates ◽  
Periodic Excitation ◽  
Identification Method ◽  
Reaction Forces ◽  
Improved Model

This paper discusses the advantages of using periodic excitation and of combining internal and external measurements in experimental robot identification. This discussion is based on the robot identification method developed by Swevers et al., a method that is recognized by industry as an effective means of robot identification that is frequently used, Hirzinger, G., Fischer, M., Brunner, B., Koeppe, R., Otter, M., Grebenstein, M., and Schafer, I, 1999, “Advances is Robotics: The DLR Experiment,” The International Journal of Robotics Research, Vol. 18, No. 11, pp. 1064–1087 [3]. Experimental results on a KUKA IR 361 show that the periodicity of the robot excitation is a key element of this method. Nonperiodic robot excitation complicates the signal processing preceding the parameter estimation, often yielding less accurate parameter estimates. An extension of this identification method combines internal and external measurements, Chenut, X., Samin, J. C., Swevers, J., and Ganseman, C., 2000, “Combining Internal and External robot Models for improved Model Parameter Estimation,” Mechanical Systems and Signal Processing. Vol. 14, No. 5, pp. 691–704 [4], yielding robot models that allow to accurately predict the actuator torques and the reaction forces/torques of the robot on its base plate, which are both important for the path planning. This paper presents and critically discusses the first experimental results obtained with this method.

Signal Parameter Estimation for Seismic Data

1992 ◽  
pp. 1797-1800
Author(s):  
D. Maiwald ◽  
D. Kraus ◽  
J.F. Böhme
Keyword(s):  
Parameter Estimation ◽  
Seismic Data ◽  
Signal Parameter
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