MIMO Radar 3D Imaging Based on Combined Amplitude and Total Variation Cost Function With Sequential Order One Negative Exponential Form

Abstract

In inverse synthetic aperture radar (ISAR) imaging, a target is usually regarded as consist of a few strong (specular) scatterers and the distribution of these strong scatterers is sparse in the imaging volume. In this paper, we propose to incorporate the sparse signal recovery method in 3D multiple-input multiple-output radar imaging algorithm. Sequential order one negative exponential (SOONE) function, which forms homotopy between 1 and 0 norms, is proposed to measure the sparsity. Gradient projection is used to solve a constrained nonconvex SOONE function minimization problem and recover the sparse signal. However, while the gradient projection method is computationally simple, it is not robust when a matrix in the algorithm is ill conditioned. We thus further propose using diagonal loading and singular value decomposition methods to improve the robustness of the algorithm. In order to handle targets with large flat surfaces, a combined amplitude and total-variation objective function is also proposed to regularize the shapes of the flat surfaces. Simulation results show that the proposed gradient projection of SOONE function method is better than orthogonal matching pursuit, CoSaMp, l1-magic, Bayesian method with Laplace prior, smoothed l0 method, and l1-ls in high SNR cases for recovery of ± 1 random spikes sparse signal. The quality of the simulated 3D images and real data ISAR images obtained using the new method is better than that of the conventional correlation method and minimum l2 norm method, and competitive to the aforementioned sparse signal recovery algorithms.

Ground-Based Radar Interferometry: A Bibliographic Review

“…A more radical solution is a two-dimensional MIMO [116], preferably based on the compressive sensing approach [117]. These are exciting developments, but they are again prototypes in a laboratory and not suitable to be used in the field.…”

Isar Imaging Based on Iterative Reweighted Lp Block Sparse Reconstruction Algorithm

“…By using the sparse signal recovery algorithm, the imaging quality can be improved. This has been successfully shown in SAR imaging [7,8], ISAR imaging [9,10], MIMO radar imaging [11,12].…”

Isar Imaging Based on L1 L0 Norms Homotopy 2d Block Sparse Signal Recovery Algorithm

“…For a one dimensional sparse signal e, define an exponential function g σ (e) = e − |e| σ and a sparse pseudo norm G σ (e) = M − m g σ (e(m)) [12,13], where M is the length of e. It had been shown that when σ approaches +∞, G σ (e) approaches 1 norm with a ratio difference; when σ approaches 0, G σ (e) approaches 0 norm. Hence, when σ moves from +∞ to 0, G σ (e) moves smoothly from 1 norm to 0 norm.…”

“…However, these two methods are computational expensive, especially of [9], where forming a 128 × 64 ISAR image needs over one hour using a prevalent personal computer. 1 0 norms homotopy based algorithm, by varying a parameter σ, builds a homotopy between 1 norm and 0 norm, and has superior performance [11,12]. Another merit is that 1 0 norms homotopy algorithm is easily extended to 2D and block sparse signal recovery cases and the computation speed is fast.…”