ABSTRACT
This chapter presents an overview of basic parameter estimation techniques and discusses the relationships among them. It introduces the Cramer-Rao lower bound (CRLB) and a general method of finding minimum variance unbiased estimator. CRLB is a lower bound on the variance of any unbiased estimator. This bound is important, as it provides a benchmark performance for any unbiased estimator. Parameter estimation is prevalent in communications and signal processing applications, for example, in channel estimation, synchronization, parametric spectral estimation and direction-of-arrival estimation. The CRLB is derived from the likelihood function, which is the probability density function of the observations viewed as a function of the unknown parameter. Newton-Raphson and steepest descent methods may indeed reduce the computational complexity compared to the grid search.
