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Introduction :: System Model :: Expectation :: Part 1 :: Filters :: Part 2 :: Submission
Introduction
System identification is the means by which systems are modeled mathematically based on measured data. It is often a precursor to other engineering tasks, such as control or signal processing. The system models are frequently described parametrically. System identification is sometimes done using only measurements of the output of the system. In this exercise, however, it is assumed that both input and output data are available. (Usually having both input and output data significantly simplifies the system identification, often resulting in straightforward linear equations to solve.)
It is usually assumed that the system measurements are made in the presence of noise. A common assumption is that the noise signals are white. However, to make things more interesting, in this assignment the noise is assumed to be from an AR process, whose parameters are also to be identified.
Your task is to take measured data and from it, determine the system and noise parameters.
