Sparse Sensing and Optimal Precision: An Integrated Framework for H₂/H_? Optimal Observer Design

In this paper, we simultaneously determine the optimal sensor precision and observer gain, which achieves specified accuracy in state estimates. Along with unknown formulation parameterizes scaling of exogenous inputs that correspond to noise. Reciprocal is defined as precision, sparseness achieved by minimizing $l_1$ norm vector. The optimization performed constraints guaranteeing estimates, are terms $\mathcal{H}_2$ or $\mathcal{H}_{\infty}$ norms error dynamics. results presented paper applied linearized longitudinal model an F-16 aircraft.