Jianxiong Zhou

High-Resolution Fully Polarimetric ISAR Imaging Based on Compressive Sensing

A 2-D range/cross-range radar image of a target is always sparse since only a few strong scattering centers occupy the whole image plane, and thus, it is quite suitable to apply the compressive sensing (CS) theory to obtain inverse synthetic aperture radar (ISAR) images. In this paper, a novel fully polarimetric ISAR imaging method based on CS is proposed. First, a definition of joint sparsity is given by exploiting the scattering characteristics of a target in fully polarimetric channels. Then, fully polarimetric ISAR images are constructed by means of the sparse recovery algorithm under the constraint of the joint sparsity. This proposed imaging method combines the merits of a full-polarization technique and CS theory, and hence, it has two main advantages: it can provide high-resolution ISAR images with limited measurements, which is a promising technique for reducing data storage; it generates fully polarimetric ISAR images with the number and the positions of the scattering centers aligned in polarimetric channels, which allows for further polarimetric scattering characteristic analysis. Finally, both simulation and experimental results are shown to demonstrate the validity of the proposed approach.

A Fast and Accurate Reconstruction Algorithm for Compressed Sensing of Complex Sinusoids

The standard compressed sensing (CS) theory reconstructs a signal by recovering a sparse representation of the signal over a pre-specified dictionary. For CS of complex sinusoids, this dictionary is usually set to be a DFT matrix corresponding to a uniform frequency grid. However, such a setting can make conventional CS reconstruction methods degrade considerably, since component frequencies of practical signals do not necessarily align with the specified grid. To deal with this problem, we apply a linear approximation to the true unknown dictionary and establish a more accurate model for sparse approximation of practical complex sinusoids. Based on this model, signal reconstruction is reformulated as a problem that recovers two sparse coefficient vectors over two known dictionaries under the constraint that the vectors share the same support. To solve such a problem, we develop a fast iterative algorithm under a variational Bayesian inference framework. Results of extensive numerical experiments demonstrate that the algorithm can achieve CS of complex sinusoids with low computational cost as well as high reconstruction accuracy.

Three-Dimensional Sparse Turntable Microwave Imaging Based on Compressive Sensing

In this letter, we propose a fast reconstruction algorithm for 3-D turntable microwave imaging from sparse measurements. A conventional Fourier-transform-based 3-D microwave imaging method collects data over densely azimuth-elevation samples and needs a large amount of data storage and long collection time. To reduce the cost of data acquisition, the proposed method exploits the sparsity in the image domain to achieve 3-D microwave imaging by utilizing sparse measurements. For this aim, the signal model is first represented as a tensor array, and then, a novel sparse reconstruction algorithm called 3-D-SL0 is applied to recover the 3-D scattering reflectivity, i.e., a 3-D image. Simulation results are finally shown to investigate the validity of the proposed method.

A Perturbation-Based Approach for Compressed Sensing Radar Imaging

This letter is concerned with two-dimensional radar imaging via compressed sensing. First, a perturbation-based sparse representation dictionary is established to alleviate the basis mismatch effect, reduce the memory requirement, and accelerate the imaging process. Then, an iterative algorithm based on variational Bayesian inference is developed for sparse recovery that is user-parameter-free and suitable for imposing a priori constraint. Experimental results based on both synthetic and real data verify the performance of the proposed approach.

Frequency-Domain Imaging Algorithm for Single-Input–Multiple-Output Array

In this letter, a novel frequency-domain imaging method is proposed for a single-input-multiple-output array, which avoids the frequency-domain interpolation. This method transforms the measurements into the wavenumber domain for compensation. The spectrum data at each frequency are proved to be the Fourier transform of the phase-modulated reflectivity function; therefore, a subimage at a specific frequency can be produced by inverse fast Fourier transform and phase demodulation. The final image is obtained by coherent accumulation of all subimages. This method does not take the plane wave approximation and can be applied in short-range imaging scenes. It has the same imaging accuracy as the backprojection algorithm but greatly reduces the computational load. Both two- and three-dimensional imaging experiments verify its performance.

Near field MIMO array imaging algorithm based on spherical wave decomposition

Multiple-input-multiple-output (MIMO) array imaging is more challenging than the monostatic configuration because the incident and reflected path are different. In FFT-based MIMO array imaging algorithms, rearrangement in wavenumber domain to reduce dimension is implemented which has low efficiency and restricts the sampling steps in spatial frequencies. This paper proposes a novel wavenumber domain imaging algorithm based on spherical wave decomposition. This algorithm uses FFT to transform the measurements into wavenumber domain for compensation, and then the spectrum is retransformed to reconstruct image by FFT and coherent accumulation. It avoids the rearrangement operation and preserves the high efficiency of the FFT-based method, and can be implemented for transmitting and receiving arrays of different length. The imaging performance is demonstrated by simulation.

Performance analysis of one dimensional radar imaging based on sparse and non-uniform frequency samplings

The performance of one dimensional radar imaging based on sparse and non-uniform frequency samplings is analyzed from the view of parameter estimation. Theoretical analysis indicates that using N sparse and non-uniform samplings instead of M uniform samplings only causes a performance deterioration of N/M, on the average, which is far better than the performance deterioration of N3/M3 if we reduce the data to N uniform samplings. This relationship is verified by numerical simulations. An algorithm to select a good set of samplings is also proposed to ensure a good performance among the possible random sampling sets. Results show that a good set tends to cover both ends of the frequency band well whereas a bad set tends to cover a shorter effective frequency band. The performance advantage of the selected good sampling set is testified using compact range data. (7 pages)