Sparse Bayesian learning in ISAR tomography imaging

Inverse synthetic aperture radar(ISAR) imaging can be regarded as a narrow-band version of the computer aided tomography(CT). The traditional CT imaging algorithms for ISAR, including the polar format algorithm(PFA) and the convolution back projection algorithm(CBP), usually suffer from the problem of the high sidelobe and the low resolution. The ISAR tomography image reconstruction within a sparse Bayesian framework is concerned. Firstly, the sparse ISAR tomography imaging model is established in light of the CT imaging theory. Then, by using the compressed sensing(CS) principle, a high resolution ISAR image can be achieved with limited number of pulses. Since the performance of existing CS-based ISAR imaging algorithms is sensitive to the user parameter, this makes the existing algorithms inconvenient to be used in practice. It is well known that the Bayesian formalism of recover algorithm named sparse Bayesian learning(SBL) acts as an effective tool in regression and classification,which uses an efficient expectation maximization procedure to estimate the necessary parameters, and retains a preferable property of the l0-norm diversity measure. Motivated by that, a fully automated ISAR tomography imaging algorithm based on SBL is proposed.Experimental results based on simulated and electromagnetic(EM) data illustrate the effectiveness and the superiority of the proposed algorithm over the existing algorithms.

Measurement and Prediction of Interfacial Area on a DistiIlation Tray

In this paper the modern electron optical equipment is used to translate the clear image of speed moving bubbles in bubbling liquid on a sieve tray into the digital information stored in computer, and the computer aided image processing technique is utilized to measure the bubble size distributions and interfacial areas under various operating conditions. And the dynamic behavior of bubbles in turbulent liquid is analyzed theoretically; the mechanism of bubble deformation and breakage is explored on the basis of Kolmogoroff′s isotropic turbulence hypothesis; the mathematical model for predicting the gas liquid interfacial area is proposed. The comparison between the simulated results and the experimental data shows that the model is higher in accuracy, simple in form and convenient in use.