The sensor scheduling problem tries to select one out of multiple available sensors at each time step to minimize a weighted sum of all the estimation errors over a certain time horizon. The problem can be solved by enumerating all the possible schedules. The complexity of such an enumeration approach grows exponentially fast as the horizon increases. In this report, by introducing some numerical relaxation parameter, we develop an efficient way to compute a suboptimal sensor schedule. It is shown that by choosing the relaxation parameter small enough, the performance of the obtained suboptimal schedule can be made arbitrarily close to the optimal one.

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