Method for increasing imaging quality of sparse constrained ghost radar

Abstract

The invention discloses a method for increasing imaging quality of a sparse constrained ghost radar. The method includes the following steps: firstly using the measured values of the sparse constrained ghost radar and the correlation of a measurement matrix in a spatial domain, by conducting spatial second order operation, and constructing a new measurement matrix which meets orthogonal constraints so as to suppress influence on image reconstruction imposed by the non-orthogonality of the measurement matrix; and using the new measurement matrix which meets orthogonal constraints and is obtained from the first step, based on existed sparse constrained conditions, converting the new measurement matrix to the problem of convex optimization, and when the conditions of convergence and stability are met, finding a L-norm-based optimal solution by the existed algorithm which matches tracking so as to increase the imaging quality of the sparse constrained ghost radar. According to the invention, the method effectively suppresses the influence on the sparse constrained ghost radar imposed by reconstruction noise and increases the imaging quality of the sparse constrained ghost radar.

Description

technical field [0001] The invention belongs to the technical field of compressed sensing, in particular to a method for improving the imaging quality of sparse constrained ghost radar. Background technique [0002] Sparse constrained ghost radar is a kind of speckle (measurement matrix) that uses rotating ground glass to produce speckle (measurement matrix) that satisfies the binomial distribution to illuminate the target. It can achieve high resolution through nonlinear constrained reconstruction at a condition much lower than the Nyquist sampling rate. High-speed image reconstruction is widely used in imaging radar technology in remote sensing, complex environment imaging and other fields. [0003] However, the binomial distribution speckle produced by the sparsely constrained ghost radar does not satisfy the orthogonality constraint, which significantly restricts the target reconstruction. At present, for the reconstruction image degradation caused by the sparse constra...

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Problems solved by technology

For example, the method proposed by Shapiro et al. (Shapiro, Jeffrey H. "Computational ghost imaging." Physical Review A78.6(2008): 061802.) using micro-mirror arrays instead of rotating frosted glass, but this method greatly limits the light source Intensity, which limits the detection distance

The optimal total variation method proposed by Zhang (Zhang, Jian, et al."Improved total variation based imagecompressive sensing recovery by nonlocal regularization."2013IEEEInternational Symposium on Circuits and Systems(ISCAS2013).IEEE,2013.), but the method calculates High complexity, slow reconstruction

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