Tolerance-weighted L-optimal experiment design: a new approach to task-directed sensing

The choice of 'where to look next' is a special case of an optimal experime nt design. This paper proposes the tolerance-optimal experiment design, whi ch is a special instance of the well-known L-optimal design, that minimizes the weighted trace of the covariance matrix of the estimated state under G aussian assumptions. The weighting matrix is chosen such that the design is invariant to transformations with non-singular Jacobians, and such that th e emerging sensing sequence reflects the information needs of the task. Thi s tolerance-optimal design does not require more calculations than existing optimal experiment designs. Existing optimal experiment designs do not ref lect the information needs of the task. In addition, some of them physicall y do not make sense if the estimated state has inconsistent units.