Fangfang Shen

Robust ISAR imaging based on compressive sensing from noisy measurements

the compressive sensing (CS) based ISAR imaging has exhibited high-resolution imaging quality when faced with limited spatial aperture. However, its performance is significantly dependent on the number of pulses and the noise level. In this paper, from the perspective of promoted sparsity constraint, a novel reconstruction model deducted from Meridian prior (MCS) is proposed. The detailed comparison of the proposed MCS model with the Laplace-prior-based CS model (LCS) is conducted. The Lorentz curve analysis testified the enhanced sparsity of the MCS model. Different from the algorithm for LCS model, in our solution procedure, the variance parameter is iteratively updated until the algorithm converges. Simulations and the ground truth data experiments of ISAR show that, with the decrease of the number of pulses and signal-to-noise ratio, the proposed model exhibits better performance in terms of resolution and amplitude error than that of the LCS model.

Wideband DOA Estimation Based on Sparse Representation in 2-D Frequency Domain

In this paper, we propose a novel sparse representation (SR)-based model for wideband direction-of-arrival (DOA) estimation. Different from the classical SR-based DOA model, which only explores the sparsity of sources in spatial space, the proposed model exhibits the sparsity through the fact that any individual wideband source is characterized as a unique oblique line passing through the origin in the 2-D frequency plane of the temporal-spatial array data. To further reduce the computation complexity during the use of such sparse property, the 2-D direction information is projected into a 1-D space. Simulations show the superior performance of our approach, even in the noise corruption and less temporal samples condition.

Compressive SAR Imaging with Joint Sparsity and Local Similarity Exploitation

Compressive sensing-based synthetic aperture radar (SAR) imaging has shown its superior capability in high-resolution image formation. However, most of those works focus on the scenes that can be sparsely represented in fixed spaces. When dealing with complicated scenes, these fixed spaces lack adaptivity in characterizing varied image contents. To solve this problem, a new compressive sensing-based radar imaging approach with adaptive sparse representation is proposed. Specifically, an autoregressive model is introduced to adaptively exploit the structural sparsity of an image. In addition, similarity among pixels is integrated into the autoregressive model to further promote the capability and thus an adaptive sparse representation facilitated by a weighted autoregressive model is derived. Since the weighted autoregressive model is inherently determined by the unknown image, we propose a joint optimization scheme by iterative SAR imaging and updating of the weighted autoregressive model to solve this problem. Eventually, experimental results demonstrated the validity and generality of the proposed approach.

SAR Imaging With Structural Sparse Representation

Sparse representation (SR)-based SAR imaging approaches have shown their superior performance compared with conventional approaches. However, for an image with rich spatial structures, a fixed global dictionary is usually ineffective to characterize the local structures. Piecewise autoregressive (PAR) model indicates that each pixel can be linearly represented by its local neighboring pixels. Inspired by this, an adaptive sparse space, effectively characterizing the varying image local structures, is designed, in which the entries are derived from the PAR model. By incorporating the adaptive SR into the SAR imaging, a novel structural SR-based SAR (SSR-SAR) imaging approach is proposed. Due to the fact that the adaptive sparse space is greatly dependent on the prior information of the SAR image, updating of the adaptive sparse space and SAR imaging is a joint optimization problem. In our approach, we propose to introduce the alternative minimization scheme to solve the problem. Besides, the Augmented Lagrangian Multiplier technique is adopted to accelerate the computation speed. Finally, experimental results are shown to demonstrate the validity of the proposed approach.

A high quality image reconstruction method based on nonconvex decoding

The article proposes a fast reconstruction algorithm for ℓ0⩽p⩽1 norm nonconvex model, called Gradient projection nonconvex sparse recovery (GPNSR), which makes a good performance in high-quality image reconstruction. We apply the GPNSR into canonical multiple description coding theory and propose a robust high-quality compressed sensing-based coding method. Combined with iterative weighted technique and fast gradient descent method, the proposed method implicitly implements matrix inverse operations to highly reduce the storage pressure. Also the weighted norm method is taken to optimize the descent steps, which highly increases convergent speed of the algorithm. Taking an image has sparse Fourier representations as an example, the paper presents the detailed image sparse coding and fast reconstruction procedure. By integrating the compressive sensing multi-description coding framework, the simulation demonstrates the superior reconstruction performance of the proposed algorithm. Most importantly, with p close to zero, the reconstruction performance of GPNSR can approximate that of ℓ0 norm optimization result.

Multiple-Stations Scalable Transmission With Compressed Sensing for Near-Space Communication

Near-space communication suffers from communication blackout, which reduces the communication quality. To solve this problem, an efficient Multiple-Stations Scalable Transmission scheme based on compressed sensing theory is proposed. The proposed scheme exhibits two advantages. First, multiple-station receivers are disposed at different locations to accomplish continuous and real-time communication with the hypersonic vehicle. Second, by utilizing the democracy and scalability of the CS, a scalable CS-based coding method is developed in which a two-layer structure is adopted to improve the coding efficiency. Finally, simulation results show that the proposed scheme can achieve a reliable communication in the presence of data loss caused by communication interruption.

SAR image despeckling with adaptive sparse representation

SR-based denoising methods have shown promising performance in image denoising. However, Because of the degradation of the noisy image, conventional SR based denoising models may not be accurate enough for the reconstruction of a clean image. Therefore, to reduce the noise corruption, a novel adaptive sparse representation based SAR image despeckling algorithm is proposed in this paper, where the noise component is considered as the coefficient residual, which equals to the difference between the actual image coefficient and the estimated coefficient. By imposing the sparsity constraint on this residual, the noise corruption can be somehow reduced. Furthermore, both the autoregressive model and the nonlocal similarity are incorporated to characterize better the image details. The experimental results demonstrate that the proposed algorithm outperforms other algorithms both subjectively and objectively.

Cauchy diversity measures: a novel methodology for enhancing sparsity in compressed sensing

As a new enchanting theory, compressed sensing (CS) demonstrates that a sparse signal can be recovered through a surprisingly small number of linear measurements by solving a problem of l 1 norm minimisation (which can be thought as a special case of the signomial diversity measures). However, the traditional CS model with l 1 norm minimisation can not fully exploit the sparsity especially when the degree of sparsity increases or the measurements number reduces. In this study, the Cauchy diversity measures is incorporated into the proposed model to deal with the above difficulties. The simulation results demonstrate that under the same condition, this new model offers a superior reconstruction precision compared with the common used signomial diversity measures.

Range-Doppler Spectrum Estimation via Sparse variational Bayesian approach

ABSTRACT In this paper, the structural sparsity of the range profile and the range-Doppler (RD) image is utilised and an effective sparse variational Bayesian approach with modified automatic relevance determination (ARD) is proposed for the RD spectrum estimation. Specifically, a probabilistic model is derived where hierarchical sparse promoting prior is imposed over the scatter coefficients. Since the real and the imaginary parts of the same complex coefficients are strongly correlated, a symmetric construction hyper-parameter is designed. Furthermore, a new hyper-parameter learning rule is induced by optimising the marginal likelihood based on Majorization-Minimization (MM) principle to further improve the accuracy and the efficiency of the proposed algorithm. Finally, the simulation results demonstrate that the proposed algorithm can achieve superior performance and high efficiency in the scenarios of low signal-to-noise ratio (SNR) and limited pulse echoes.

Fast ISAR Imaging Based on Enhanced Sparse Representation Model

Traditional sparse representation-(SR) based inverse synthetic aperture radar (ISAR) imaging schemes can achieve significant performance, but they suffer from high costs of memory and computational complexity, because the SR of a 2-D image is converted into that of a 1-D vector. Instead of memory consuming vector operations, we propose a fast ISAR imaging algorithm, where the decomposition and reconstruction of a 2-D scene is implemented using matrix operations directly. Besides the spatial sparsity of a scene, its structural sparsity is presented using the range profile of the scene, where both can be used to enhance sparsity exploitation during image reconstruction. Also, benefitting from the structural sparsity of the range profile, the target energy can be accumulated during the process, which further improves performance. Compared to available SR-based ISAR imaging algorithms, the proposed algorithm reduces both memory costs and computational complexity significantly, which is proven using simulated and real data.