Recursive estimation and residuals

Invariance Principles for Recursive Residuals

Distributed and Recursive Estimation

Estimation is a canonical problem in sensor networks. The intrinsic nature of sensor networks requires estimation algorithms based on sensor data to be distributed and recursive; such algorithms are studied in this chapter for the problem of (conditional) least squares estimation. The chapter is divided into three parts. In the first part, distributed and recursive estimation algorithms are dev...

Recursive Least Squares Estimation

We start with estimation of a constant based on several noisy measurements. Suppose we have a resistor but do not know its resistance. So we measure it several times using a cheap (and noisy) multimeter. How do we come up with a good estimate of the resistance based on these noisy measurements? More formally, suppose x = (x1, x2, . . . , xn) T is a constant but unknown vector, and y = (y1, y2, ...

Recursive single frequency estimation

A new divide-and-conquer method for estimating the frequency of a single complex sinusoid in additive uncorrelated noise is proposed. Its computational complexity is comparable to previous fast methods (roughly 2N complex multiplies and log2(N) arctangents for N a power of 2). However, it nearly achieves the Cramer-Rao bound for a wider range of input frequency and signal-to-noise-ratio (SNR) v...

Approximate Bayesian recursive estimation

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