Recursive consistent estimation with bounded noise
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 far 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.
dithered quantization consistent reconstruction frames overcomplete representations overdetermined linear equations