Asymptotic maximum likelihood estimator performance for chaotic signals in noise

Authors:
S. Kay
Affiliations:
Dept. of Electr. Eng., Rhode Island Univ., Kingston, RI, USA
Venue:
IEEE Transactions on Signal Processing
Year:
1995

Citing 0
Cited 2

Multiple Composite Hypothesis Testing: A Competitive Approach

Journal of VLSI Signal Processing Systems
Optimal estimation and sequential channel equalization algorithms for chaotic communications systems

EURASIP Journal on Applied Signal Processing

Quantified Score

Hi-index	35.68

Visualization

Abstract

The performance of the maximum likelihood estimator for a 1-D chaotic signal in white Gaussian noise is derived. It is found that the estimator is inconsistent and therefore the usual asymptotic distribution (large data record length) is invalid. However, for high signal-to-noise ratios (SNRs), the maximum likelihood estimator is asymptotically unbiased and attains the Cramer-Rao lower bound.