High Resolution Turntable Radar Imaging via Two Dimensional Deconvolution with Matrix Completion

Abstract

Resolution is the bottleneck for the application of radar imaging, which is limited by the bandwidth for the range dimension and synthetic aperture for the cross-range dimension. The demand for high azimuth resolution inevitably results in a large amount of cross-range samplings, which always need a large number of transmit-receive channels or a long observation time. Compressive sensing (CS)-based methods could be used to reduce the samples, but suffer from the difficulty of designing the measurement matrix, and they are not robust enough in practical application. In this paper, based on the two-dimensional (2D) convolution model of the echo after matched filter (MF), we propose a novel 2D deconvolution algorithm for turntable radar to improve the radar imaging resolution. Additionally, in order to reduce the cross-range samples, we introduce a new matrix completion (MC) algorithm based on the hyperbolic tangent constraint to improve the performance of MC with undersampled data. Besides, we present a new way of echo matrix reconstruction for the situation that only partial cross-range data are observed and some columns of the echo matrix are missing. The new matrix has a better low rank property and needs just one operation of MC for all of the missing elements compared to the existing ways. Numerical simulations and experiments are carried out to demonstrate the effectiveness of the proposed method.

Through-Wall UWB Radar Based on Sparse Deconvolution with Arctangent Regularization for Locating Human Subjects

“…Given the limitation of L2 norm regularization and the noisy characteristic of received signal y , we can easily estimate x to be a sparse signal (spike) from y by minimizing Equation (10) with a convex regularization term of the L1 norm [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ], . where is called L1 norm regularization (convex regularization) represented by the sum of absolute values of vector x , .…”

“…Basic deconvolution is the process of extracting the unknown input signal ( x ) of a linear time-invariant system ( y = Hx in matrix form) when the noise-free output signal ( y ) and wavelet ( H ) are known. However, in real-world applications, the output signal ( y ) is noisy and distorted by inhomogeneous media, such as ground-penetrating radar (GPR) [ 1 , 2 ], seismicity [ 3 , 4 ], radars [ 5 , 6 , 7 , 8 ], astronomy [ 9 ], speech recognition [ 10 , 11 ], and image reconstruction [ 12 , 13 , 14 , 15 ]. Nowadays, sparse deconvolution plays an important role in extracting the original data from the noisy received signal; it has been widely used in denoising, interpolation, super-resolution, and declipping [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ].…”