Douglas Cochran

Alfred O. Hero, Douglas Cochran

Sensor systems typically operate under resource constraints that prevent the simultaneous use of all resources all of the time. Sensor management becomes relevant when the sensing system has the capability of actively managing these resources; i.e., changing its operating configuration during deployment in reaction to previous measurements. Examples of systems in which sensor management is currently used or is likely to be used in the near future include autonomous robots, surveillance and reconnaissance networks, and waveform-agile radars. This paper provides an overview of the theory, algorithms, and applications of sensor management as it has developed over the past decades and as it stands today.

Rosemary A. Renaut, Michael Horst, Yang Wang et al.

The solution, $x$, of the linear system of equations $A x\approx b$ arising from the discretization of an ill-posed integral equation with a square integrable kernel $H(s,t)$ is considered. The Tikhonov regularized solution $ x(λ)$ is found as the minimizer of $J( x)=\{ \|A x - b\|_2^2 + λ^2 \|L x\|_2^2\}$. $ x(λ)$ depends on regularization parameter $λ$ that trades off the data fidelity, and on the smoothing norm determined by $L$. Here we consider the case where $L$ is diagonal and invertible, and employ the Galerkin method to provide the relationship between the singular value expansion and the singular value decomposition for square integrable kernels. The resulting approximation of the integral equation permits examination of the properties of the regularized solution $ x(λ)$ independent of the sample size of the data. We prove that estimation of the regularization parameter can be obtained by consistently down sampling the data and the system matrix, leading to solutions of coarse to fine grained resolution. Hence, the estimate of $λ$ for a large problem may be found by downsampling to a smaller problem, or to a set of smaller problems, effectively moving the costly estimate of the regularization parameter to the coarse representation of the problem. Moreover, the full singular value decomposition for the fine scale system is replaced by a number of dominant terms which is determined from the coarse resolution system, again reducing the computational cost. Numerical results illustrate the theory and demonstrate the practicality of the approach for regularization parameter estimation using generalized cross validation, unbiased predictive risk estimation and the discrepancy principle applied for both the system of equations, and the augmented system of equations.

Bill Moran, Fred Cohen, Zengfu Wang et al.

We consider the problem of fusing measurements from multiple sensors, where the sensing regions overlap and data are non-negative---possibly resulting from a count of indistinguishable discrete entities. Because of overlaps, it is, in general, impossible to fuse this information to arrive at an accurate estimate of the overall amount or count of material present in the union of the sensing regions. Here we study the range of overall values consistent with the data. Posed as a linear programming problem, this leads to interesting questions associated with the geometry of the sensor regions, specifically, the arrangement of their non-empty intersections. We define a computational tool called the fusion polytope and derive a condition for this to be in the positive orthant thus simplifying calculations. We show that, in two dimensions, inflated tiling schemes based on rectangular regions fail to satisfy this condition, whereas inflated tiling schemes based on hexagons do.