Noname manuscript No. (will be inserted by the editor) An Outer-Approximation Approach for Information- Maximizing Sensor Selection Han-Lim Choi · Jonathan P. How · Paul I. Barton Received: date / Accepted: date Abstract This paper addresses information-maximizing sensor selection that deter- mines a set of measurement locations providing the largest entropy reduction in the estimates of the state variables. A new mixed-integer semidefinite program (MISDP) formulation is proposed for this selection under the constraints resulting from com- munication limitations. This formulation employs binary variables indicating if the corresponding measurement location is selected, and ensures convexity of the objective function and linearity of the constraint functions by exploiting the linear equivalent form of a bilinear term involving binary variables. An outer-approximation algorithm is then developed for the MISDP formulation that obtains the global optimal solution by solving a sequence of mixed-integer linear programs for which reliable solvers are available. Numerical experiments verify the solution optimality and the computational effectiveness of the proposed algorithm by comparing it with an existing branch-and- bound method. An example of sensor selection to track a moving target is considered to demonstrate the applicability of the proposed method and highlight its ability to handle quadratic constraints. Keywords sensor selection · outer-approximation algorithm · mutual information · maximum entropy sampling H.-L. Choi 335 Gwahangno, Rm N7-2-4309, Yuseong, Daejeon 305-701, Korea Tel.: +82-42-350-3727 Fax: +82-42-350-3710 E-mail: hanlimc@kaist.ac.kr J. P. How 77 Massachusetts Ave., Rm 33-326, Cambridge, MA 02139, USA Tel.: +1-617-253-3267 Fax: +1-617-253-7397 E-mail: jhow@mit.edu P. I. Barton 77 Massachusetts Ave., Rm 66-464, Cambridge, MA 02139, USA Tel.: +1-617-253-6526 Fax: +1-617-258-7864 E-mail: pib@mit.edu