28 July 1997 Multisensor remote sensing in scattering media via fusing experiment design and regularization theory methods
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In this paper we address a new approach to the remote sensing imaging problems for radar/sonar array imaging system stated and treated as ill-posed inverse problems of restoration the extended object reflected signals distorted in a stochastic scattering medium. The developed approach is based on combining the Bayesian estimation technique for signal restoration problems with constrained regularization technique for inversion of the signal formation operator of the stochastic measurement channel. To reduce the generic ill-posed imaging problem to its radar/sonar system oriented numerical version the experiment design methodology is applied. This results in the projection-dependent scheme for measured data that originates from limited number of sensors of a sparse array. Next, to alleviate the limitations on the absence of prior knowledge ofthe object signal the model-based assumptions for the desired image space are introduced. Model-based fusion of such diverse information on data sets and image space in a generalized constrained array imaging inverse problem is the first issue addressed in the paper. Optimal/suboptimal solution of this problem in the mixed Bayesian-regularization setting that results in the development of numerical technique for extended object imaging in scattering random media with improved spatial resolution is the second issue this paper addresses. Some computer simulation results are also provided to illustrate the proposed approach.
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Yuri V. Shkvarko, Yuri V. Shkvarko, } "Multisensor remote sensing in scattering media via fusing experiment design and regularization theory methods", Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997); doi: 10.1117/12.280812; https://doi.org/10.1117/12.280812

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