Hopefully you are familiar with the notion of the eigenvectors of a "matrix system," if not they do a quick review of eigen-stuff. We can develop the same ideas for LTI systems acting on signals. A linear time invariant (LTI) system ℋ ℋ operating on a continuous input f⁢t f t to produce continuous time output y⁢t y t

is mathematically analogous to an NNxNN matrix A A operating on a vector x∈ ℂ N x ℂ N to produce another vector b∈ ℂ N b ℂ N (see Matrices and LTI Systems for an overview).

Just as an eigenvector of A A is a v∈ ℂ N v ℂ N such that A⁢v=λ⁢v A v λ v , λ∈C λ ,

we can define an eigenfunction (or eigensignal) of an LTI system ℋ ℋ to be a signal f⁢t f t such that

Eigenfunctions are the simplest possible signals for ℋ ℋ to operate on: to calculate the output, we simply multiply the input by a complex number λ λ.

The class of LTI systems has a set of eigenfunctions in common: the complex exponentials es⁢t s t , s∈C s are eigenfunctions for all LTI systems.

We can prove Equation 4 by expressing the output as a convolution of the input es⁢t s t and the impulse response h⁢t h t of ℋ ℋ:

Since the expression on the right hand side does not depend on t t, it is a constant, λ s λ s . Therefore The eigenvalue λ s λ s is a complex number that depends on the exponent s s and, of course, the system ℋ ℋ. To make these dependencies explicit, we will use the notation H⁢s≡ λ s H s λ s .

Since the action of an LTI operator on its eigenfunctions es⁢t s t is easy to calculate and interpret, it is convenient to represent an arbitrary signal f⁢t f t as a linear combination of complex exponentials. The Fourier series gives us this representation for periodic continuous time signals, while the (slightly more complicated) Fourier transform lets us expand arbitrary continuous time signals.