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Overview A key objective of NASA’s Mars Exploration program (MEP) is

Propagation and scattering model. System model. Modeled radar return. Model-Based Algorithm. Actual radar return. Model Based Signal Processing for MIDP Radar. Block Diagram of the Model-Based Approach. Overview A key objective of NASA’s Mars Exploration program (MEP) is

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Overview A key objective of NASA’s Mars Exploration program (MEP) is

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  1. Propagation and scattering model System model Modeled radar return Model-Based Algorithm Actual radar return Model Based Signal Processing for MIDP Radar Block Diagram of the Model-Based Approach • Overview • A key objective of NASA’s Mars Exploration program(MEP) is • to characterize the surface and sub-surface features of Mars. • Permittivity contrast in layered media causes reflection of incident EM Wave. • We need a robust EM inversion scheme to estimate the • multi-layered media-properties. • Permittivity profile can be obtained by minimizing the mean • square error (MSE) between measured and modeled data. • Challenges: • - Radar return is corrupted by noise & scattering components. • Need to account for system effects. • MSE minimization is a non-linear problem Inversion • algorithms can produce unstable /un-useful solutions. Gauss-Newton EM Inversion Algorithm Enhanced sub-surface features Estimated Permittivity profile The Iterative Gauss-Newton EM Inversion Algorithm Simulation Results Start • Gauss-Newton method linearizes the non-linear model S(Mc). • Lossy part of the media is assumed to be known. • Starting values of permittivity are obtained using Layer-Stripping Approach. • Plane Wave approximation is applied for forward modeling S(Mc). • Permittivity model is parameterized using a basis of delta/B-spline functions. • Discretization in depth locates the interfaces between homogeneous layers. • Optimization solution needs to be constrained to avoid unstable solutions. Get Observed radar return So . Type of radar: FMCW radar • . Freq range: 2-8 GHz • . Duration of chirp: 10 mS • . Free space Range resolution: 2.5cm • . Depth sampling for inversion: 2cm • . SNR of simulated data: 10 dB • . Number of runs: 15 • . Max. number of iterations: 400 Get starting values of permittivity (mo) Simulate return signal S(Mc) Calculate Mean square error (MSE) between S(Mc) and So Update model parameters (mk+1) using iterative Gauss- Newton method Is the MSE a Global Minimum? Stop

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