  We address the problem of determining optimal sensor precisions for
estimating the states of linear time-varying discrete-time stochastic dynamical
systems, with guaranteed bounds on the estimation errors. This is performed in
the Kalman filtering framework, where the sensor precisions are treated as
variables. They are determined by solving a constrained convex optimization
problem, which guarantees the specified upper bound on the posterior error
variance. Optimal sensor precisions are determined by minimizing the l1 norm,
which promotes sparseness in the solution and indirectly addresses the sensor
selection problem. The theory is applied to realistic flight mechanics and
astrodynamics problems to highlight its engineering value. These examples
demonstrate the application of the presented theory to a) determine redundant
sensing architectures for linear time invariant systems, b) accurately estimate
states with low-cost sensors, and c) optimally schedule sensors for linear
time-varying systems.

Systems and Control;Systems and Control;Signal Processing;Optimization and Control