Statistical analysis of the product high-order ambiguity function

被引：13

作者：

Scaglione, A ^{[1
]}

Barbarossa, S ^{[1
]}

机构：

[1] Univ Roma La Sapienza, INFOCOM Dept, I-00184 Rome, Italy

来源：

IEEE TRANSACTIONS ON INFORMATION THEORY | 1999年 / 45卷 / 01期

关键词：

high-order ambiguity function; parameter estimation; polynomial-phase signals;

D O I：

10.1109/18.746840

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The high-order ambiguity function (HAF) was introduced for the estimation of polynomial-phase signals (PPS) embedded in noise. Since the HAF is a nonlinear operator, it suffers from noise-masking effects and from the appearance of undesired cross terms and, possibly, spurious harmonics in the presence of multicomponent (mc) signals. The product HAF (PHAF) was then proposed as a way to improve the performance of the HAF in the presence of noise and to solve the ambiguity problem. In this correspondence we derive a statistical analysis of the PHAF in the presence of additive white Gaussian noise (AWGN) valid for high signal-to-noise ratio (SNR) and a finite number of data samples. The analysis is carried out in detail for single-component PPS but the multicomponent case is also discussed. Error propagation phenomena implicit in the recursive structure of the PHAF-based estimator are explicitly taken into account. The analysis is validated by simulation results for both single- and multicomponent PPS's.

引用

页码：343 / 356

页数：14

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