Detection and Parameter Estimation of Multiple NonGaussian Sources via Higher Order Statistics

Sanyogita Shamsunder, Georgios B. Giannakis

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Simultaneous detection of signals arriving at a sensor array and estimation of their parameters is carried out using higher than second-order statistics. Information theoretic criteria which are (at least theoretically) insensitive to additive Gaussian noise are developed to estimate consistently the parameters as well as the number of non-Gaussian but unknown sources. The novel cumulant based algorithms can estimate parameters of more sources with fewer sensors. Simulations confirm superior resolution capability of the proposed methods for both narrow-band and wideband sources in the presence of low SNR additive correlated Gaussian noise.

Original languageEnglish (US)
Pages (from-to)1145-1155
Number of pages11
JournalIEEE Transactions on Signal Processing
Volume42
Issue number5
DOIs
StatePublished - May 1994
Externally publishedYes

Bibliographical note

Funding Information:
Manuscript received July 8, 1992; revised June 28, 1993. The associate editor coordinating the review of this paper and approving it for .publication was Prof. Daniel Fuhrmann. This work was supported by LABCOM contract 5-25254 and NSFMIP-9210230.

Fingerprint

Dive into the research topics of 'Detection and Parameter Estimation of Multiple NonGaussian Sources via Higher Order Statistics'. Together they form a unique fingerprint.

Cite this