Conference Paper

Optimal sensor configuration in remote image formation

Dept. of Electr. & Comput. Eng., Illinois Univ., Champaign, IL, USA
DOI: 10.1109/ICIP.2005.1530025 Conference: Proceedings of IEEE International Conference on Image Processing, 2005. Proc IEEE ICIP 2005, Volume: 2
Source: IEEE Xplore

ABSTRACT Determination of optimal sensor configuration is an important issue in many remote imaging modalities such as tomographic and interferometric imaging. In this paper, a statistical optimality criterion is defined and a search is performed over the space of candidate sensor locations to determine the configuration that optimizes the criterion over all candidates. To make the search process computationally feasible, a modified version of a previously proposed sub-optimal backward greedy algorithm is used. Connections of the method to the deterministic backward greedy algorithm for the subset selection problem are presented and a conjecture on the optimality of the proposed method is suggested. Three compelling optimality criteria are introduced and their performance is investigated through numerical experiments for a tomographic imaging scenario. In all cases, it is verified that the chosen configuration by the proposed algorithm performs better than wisely chosen alternatives.

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    • "Initially , contains all of the candidate sensor locations, i.e., . The CSBS algorithm can be formally written as [28] (23) with the stopping criterion being where is the desired number of sensors as mentioned in (5). "
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    ABSTRACT: Determination of optimal sensor configuration is an important issue in many remote imaging modalities, such as tomographic and interferometric imaging. In this paper, a statistical optimality criterion is defined and a search is performed over the space of candidate sensor locations to determine the configuration that optimizes the criterion over all candidates. To make the search process computationally feasible, a modified version of a previously proposed suboptimal backward greedy algorithm is used. A statistical framework is developed which allows for inclusion of several widely used image constraints. Computational complexity of the proposed algorithm is discussed and a fast implementation is described. Furthermore, upper bounds on the sum of the squared error of the proposed algorithm are derived. Connections of the method to the deterministic backward greedy algorithm for the subset selection problem are presented, and two application examples are described. Five compelling optimality criteria are considered, and their performance is investigated through numerical experiments for a tomographic imaging scenario. In all cases, it is verified that the configuration designed by the proposed algorithm performs better than wisely chosen alternatives.
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