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 In proceeding of: Image Processing, 2005. ICIP 2005. IEEE International Conference on, Volume: 2 Source: IEEE Xplore

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**ABSTRACT:**A tomographic forward and inverse model is presented that enables one to recover the 3D structure of the ionosphere from observations out of the plane of the orbit. In this paper we apply the technique to the Global Ultraviolet Imager (GUVI) on-board the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite. The forward model is based on GUVI observation geometry to simulate radiance observations of a model ionosphere. This model incorporates the physics of the 135 A emission and the pattern of line-of-sight measurements into a discrete matrix representation of the GUVI observation. The application of matrix inversion techniques to the discrete observation matrix allows a multi-dimensional electron density profile to be reconstructed from the GUVI brightness measurements. Appropriate regularization functionals are incorporated to constrain the reconstructed solution. A smoothness constraint with a non-convex penalty function ensures smoothness while preserving edges in the reconstructed image, an attribute which is crucial for the reconstruction of sharp ionospheric gradients. Results using GUVI data are shown to verify the applicability of this technique.Radio Science 01/2007; 42(2). · 1.00 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**A tomographic forward and inverse model is presented for deriving 3-D images of ionospheric electron density from ground-based dual-frequency Global Positioning System (GPS) measurements and ionosonde data. The GPS observation geometry is discretely modeled, and a linear algebraic relationship is derived between the integrated electron density measurements and the ionospheric electron density. Because the inverse problem is ill conditioned, regularization is used to stabilize the solution in the presence of noise. In this paper, we regularize the inverse problem by incorporating neighborhood smoothness and continuity constraints applicable to general ionospheric conditions. To avoid oversmoothing of edges, nonconvex regularizing functionals are used to capture potential localized ionospheric density structures. A deterministic relaxation technique is used to minimize the proposed cost function. The specific formulation of the reconstruction geometry is directly related to the sparseness and the nonuniform distribution of the GPS ray paths. The grid boundaries, the regularization parameters, the model order, and the grid placement are selected in conjunction with available remote sensing data and appropriate optimality criteria. The algorithm is tested using simulations of ionospheric structures with actual GPS observation geometry. These simulations demonstrate the effectiveness in detecting and reconstructing ionospheric height and density fluctuations, and illustrate the statistical performance and bounds of the inversion technique.IEEE Transactions on Geoscience and Remote Sensing 02/2009; · 3.47 Impact Factor - [Show abstract] [Hide abstract]

**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.IEEE Transactions on Image Processing 03/2008; 17(2):155-66. · 3.20 Impact Factor

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