Differential sampling for fast frequency acquisition via adaptive extended least squares algorithm
Abstract
This paper presents a differential signal model along with appropriate sampling techinques for least squares estimation of the frequency and frequency derivatives and possibly the phase and amplitude of a sinusoid received in the presence of noise. The proposed algorithm is recursive in mesurements and thus the computational requirement increases only linearly with the number of measurements. The dimension of the state vector in the proposed algorithm does not depend upon the number of measurements and is quite small, typically around four. This is an advantage when compared to previous algorithms wherein the dimension of the state vector increases monotonically with the product of the frequency uncertainty and the observation period. Such a computational simplification may possibly result in some loss of optimality. However, by applying the sampling techniques of the paper such a possible loss in optimality can made small.
 Publication:

ITC/USA/'87; Proceedings of the International Telemetering Conference
 Pub Date:
 1987
 Bibcode:
 1987isa..conf..191K
 Keywords:

 Adaptive Filters;
 Data Acquisition;
 Data Sampling;
 Least Squares Method;
 Digital Data;
 Fast Fourier Transformations;
 Kalman Filters;
 Recursive Functions;
 Signal To Noise Ratios;
 Communications and Radar