Product high-order ambiguity function for multicomponent polynomial-phase signal modeling

Sergio Barbarossa, Anna Scaglione, Georgios B. Giannakis

Research output: Contribution to journalArticlepeer-review

324 Scopus citations

Abstract

Parameter estimation and performance analysis issues are studied for multicomponent polynomial-phase signals (PPS's) embedded in white Gaussian noise. Identiflability issues arising with existing approaches are described first when dealing with multicomponent PPS having the same highest order phase coefficients. This situation is encountered in applications such as synthetic aperture radar imaging or propagation of polynomialphase signals through channels affected by multipath and is thus worthy of a careful analysis. A new approach is proposed based on a transformation called product high-order ambiguity function (PHAF). The use of the PHAF offers a number of advantages with respect to the high-order ambiguity function (HAF). More specifically, it removes the identifiability problem and improves noise rejection capabilities. Performance analysis is carried out using the perturbation method and verified by simulation results.

Original languageEnglish (US)
Pages (from-to)691-708
Number of pages18
JournalIEEE Transactions on Signal Processing
Volume46
Issue number3
DOIs
StatePublished - 1998

Bibliographical note

Funding Information:
Manuscript received July 25, 1996; revised September, 26, 1997. This work was supported by the “Ministero per l’Universita’ e la Ricerca Scientifica e Tecnologica” (MURST), Italy, and by ONR Grant N0014-93-1-0485. The associate editor coordinating the review of this paper and approving it for publication was Dr. Jitendra K. Tugnait.

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