Robust parameter estimation of a deterministic signal in impulsive noise

被引:24
作者
Friedmann, J [1 ]
Messer, H
Cardoso, JF
机构
[1] Tel Aviv Univ, Dept Elect Syst Engn, IL-69978 Tel Aviv, Israel
[2] Ecole Natl Super Telecommun, CNRS, Dept TSI, Paris, France
关键词
alpha-stable distribution; impulsive noise; parameter estimation; robust estimation;
D O I
10.1109/78.827528
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents a robust class of estimators for the parameters of a deterministic signal in impulsive noise. The proposed technique has the structure of the maximum likelihood estimator (MLE) but has an extra degree of freedom: the choice of a nonlinear function (which is different from the score function suggested by the MLE) that can be adjusted to improve robustness, The effect of this nonlinear function is studied analytically via an asymptotic performance analysis. We investigate the covariance of the estimates and the loss of efficiency induced by nonoptimal choices of the nonlinear function, giving special attention to the case of cc-stable noise. Finally, we apply the theoretical results to the problem of estimating parameters of a sinusoidal signal in impulsive noise.
引用
收藏
页码:935 / 942
页数:8
相关论文
共 11 条
[1]  
FAMMA EF, 1971, J AM STAT ASSOC, V66, P331
[2]  
GALIN L, 1996, THESIS TEL AVIV U TE
[3]  
Huber P. J., 1981, ROBUST STAT
[4]   Adaptive weighted myriad filter algorithms for robust signal processing in α-stable noise environments [J].
Kalluri, S ;
Arce, GR .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 1998, 46 (02) :322-334
[5]  
Kassam SA., 2012, SIGNAL DETECTION NON
[6]  
Kay S. M., 1998, Fundamentals of Statistical Signal Processing, Volume 1:Estimation Theory, V1
[7]   AN ITERATIVE PROCEDURE FOR THE ESTIMATION OF THE PARAMETERS OF STABLE LAWS [J].
KOUTROUVELIS, IA .
COMMUNICATIONS IN STATISTICS PART B-SIMULATION AND COMPUTATION, 1981, 10 (01) :17-28
[8]  
Kuruoglu E., 1998, P EUSIPCO 98, P989
[9]  
Nikias CL., 1995, SIGNAL PROCESSING AL
[10]  
Samoradnitsky G., 1994, Stable Non-Gaussian Random Processes