Optimal input design for system identification using spectral decomposition
It addresses the problem of optimal input design for system identification, offering a computationally efficient approach with guaranteed optimality bounds.
This paper proposes a method for designing band-limited optimal inputs for linear MIMO system identification using spectral decomposition, achieving global optimality in many cases as demonstrated by matching lower and upper bounds.
The aim of this paper is to design a band-limited optimal input with power constraints for identifying a linear multi-input multi-output system. It is assumed that the nominal system parameters are specified. The key idea is to use the spectral decomposition theorem and write the power spectrum as $ϕ_{u}(jω)=\frac{1}{2}H(jω)H^*(jω)$. The matrix $H(jω)$ is expressed in terms of a truncated basis for $\mathcal{L}^2\left(\left[-ω_{\mbox{cut-off}},ω_{\mbox{cut-off}}\right]\right)$. With this parameterization, the elements of the Fisher Information Matrix and the power constraints turn out to be homogeneous quadratics in the basis coefficients. The optimality criterion used are the well-known $\mathcal{D}-$optimality, $\mathcal{A}-$optimality, $\mathcal{T}-$optimality and $\mathcal{E}-$optimality. The resulting optimization problem is non-convex in general. A lower bound on the optimum is obtained through a bi-linear formulation of the problem, while an upper bound is obtained through a convex relaxation. These bounds can be computed efficiently as the associated problems are convex. The lower bound is used as a sub-optimal solution, the sub-optimality of which is determined by the difference in the bounds. Interestingly, the bounds match in many instances and thus, the global optimum is achieved. A discussion on the non-convexity of the optimization problem is also presented. Simulations are provided for corroboration.