O'Reilly logo

Lessons in Estimation Theory for Signal Processing, Communications, and Control, Second Edition by Jerry M. Mendel - Department of Electrical Engineering, University of Southern California, Los Angeles, California

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Lesson 9 Best Linear Unbiased Estimation

Summary

The main purpose of this lesson is to develop our second estimator. It is both unbiased and efficient by design and is a linear function of the measurements Z(k). It is called a best linear unbiased estimator (BLUE).

As in the derivation of the WLSE, we begin with our generic linear model; but now we make two assumptions about this model: (1) H(k) must be deterministic, and (2) V(k) must be zero mean with positive definite known covariance matrix R(k). The derivation of the BLUE is more complicated than the derivation of the WLSE because of the design constraints; however, its performance analysis is much easier because we build good performance into its design.

A very remarkable connection exists ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required