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This paper addresses the task of sensor selection over a finite time horizon for systems modeled via discrete-time, linear state-space representations. Our method for this general linear setting accommodates both spatial and temporal noise correlations. To our best knowledge, this is the first work to do so. Scheduling policies are designed to limit sensor usage and minimize a minimum-mean-square-error-based criterion with time-varying weights to accommodate different user scenarios (e.g., prioritizing certain state elements at certain times or performing linear quadratic Gaussian control). The approach is also nonmyopic since the effects of sensor activations on all time steps are incorporated. A new but algebraically equivalent formulation of the scheduling model is introduced that readily accounts for colored noise sequences. This lends a closed-form expression for the error covariance that is explicit in all scheduling variables. Such an expression had been considered intractable for filtering in both white and colored noise regimes. This expression is leveraged to develop a well-motivated surrogate objective function that is shown to be submodular, thus enabling the use of an efficient greedy algorithm accompanied by performance guarantees with respect to the surrogate objective. Numerical examples are provided to demonstrate the effectiveness of the proposed methodology. 

Abstract We examined rapid variations in the electron zebra stripe patterns, specifically atL = 1.5, over a three‐month duration, using twin Van Allen Probes within Earth's inner magnetosphere. During geomagnetically quiet intervals, these stripes exhibit a peak‐to‐valley ratio (Δj) ∼1.25 in detrended electron fluxes. However, during geomagnetic storms, they became highly prominent, with Δj > 2.5. The correlation between Δjand net field‐aligned currents (FACs) is observed to be high (0.70). Global magnetohydrodynamic (MHD) simulation results indicate that the westward electric field at midnight at low latitudes in the deep inner magnetosphere correlates well with net FACs. An increase in net FACs could amplify the dawn‐to‐dusk electric field in the deep inner magnetosphere, thereby causing the inward transport of electrons. Given that FACs are linked to the interaction between solar wind and the magnetosphere, our findings emphasize the importance of solar wind‐magnetosphere coupling in the deeper regions of the inner magnetosphere. 

Although perception is an increasingly dominant portion of the overall computational cost for autonomous systems, only a fraction of the information perceived is likely to be relevant to the current task. To alleviate these perception costs, we develop a novel simultaneous perception–action design framework wherein an agent senses only the task-relevant information. This formulation differs from that of a partially observable Markov decision process, since the agent is free to synthesize not only its policy for action selection but also its belief-dependent observation function. The method enables the agent to balance its perception costs with those incurred by operating in its environment. To obtain a computationally tractable solution, we approximate the value function using a novel method of invariant finite belief sets, wherein the agent acts exclusively on a finite subset of the continuous belief space. We solve the approximate problem through value iteration in which a linear program is solved individually for each belief state in the set, in each iteration. Finally, we prove that the value functions, under an assumption on their structure, converge to their continuous state-space values as the sample density increases.