Title :
Polynomial prediction using incomplete data
Author_Institution :
Lab. of Telecommun. Technol., Helsinki Univ. of Technol., Espoo
fDate :
3/1/1997 12:00:00 AM
Abstract :
We derive an FIR polynomial predictor for data in which some samples are missing. The method is compared with a computationally lighter algorithm that is based on decision-driven recursion. Both schemes are found to perform almost identically well on predicting a sinusoidal signal corrupted by both impulsive and Gaussian noise
Keywords :
FIR filters; Gaussian noise; computational complexity; digital filters; polynomials; prediction theory; signal sampling; FIR polynomial predictor; Gaussian noise; decision-driven recursion; impulsive noise; incomplete data; polynomial prediction; sinusoidal signal; Additive noise; Autocorrelation; Filtering; Finite impulse response filter; Gaussian noise; Polynomials; Predictive models; Radio communication; Signal processing; Signal processing algorithms;
Journal_Title :
Signal Processing, IEEE Transactions on
