Title :
Recursive consistent estimation with bounded noise
Author :
Rangan, Sundeep ; Goyal, Vivek K.
Author_Institution :
Flarion Technol., Bedminster, NJ, USA
fDate :
1/1/2001 12:00:00 AM
Abstract :
Estimation problems with bounded, uniformly distributed noise arise naturally in reconstruction problems from over complete linear expansions with subtractive dithered quantization. We present a simple recursive algorithm for such bounded-noise estimation problems. The mean-square error (MSE) of the algorithm is “almost” O(1/n 2), where n is the number of samples. This rate is faster than the O(1/n) MSE obtained by standard recursive least squares estimation and is optimal to within a constant factor
Keywords :
mean square error methods; quantisation (signal); recursive estimation; signal reconstruction; MSE; bounded noise; bounded-noise estimation problems; mean-square error; over complete linear expansions; reconstruction problems; recursive consistent estimation; recursive least squares estimation; subtractive dithered quantization; uniformly distributed noise; Additive white noise; Equations; Least squares approximation; Linear systems; Maximum likelihood estimation; Quantization; Random variables; Recursive estimation; Signal analysis; Vectors;
Journal_Title :
Information Theory, IEEE Transactions on
