Introduction to Estimation Theory

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Author:

Don Johnson

Subject:

Mathematics and Statistics, Science and Technology

Institution Name:

Connexions

Collection:

Connexions

Grade Level:

Post-secondary

Abstract:

This module introduces estimation theory and its terminology, including bias, consistency, and efficiency. In searching for methods of extracting information from noisy observations, this chapter describes estimation theory, which has the goal of extracting from noise-corrupted observations the values of disturbance parameters (noise variance, for example), signal parameters (amplitude or propagation direction), or signal waveforms. Estimation theory assumes that the observations contain an information-bearing quantity, thereby tacitly assuming that detection-based preprocessing has been performed (in other words, do I have something in the observations worth estimating?). Conversely, detection theory often requires estimation of unknown parameters: Signal presence is assumed, parameter estimates are incorporated into the detection statistic, and consistency of observations and assumptions tested. Consequently, detection and estimation theory form a symbiotic relationship, each requiring the other to yield high-quality signal processing algorithms.

Course Type:

Learning Module

Languages:

English

Material Type:

Readings, Syllabi

Media Format:

Text/HTML

Conditions of Use:

Creative Commons Attribution 1.0