Bayesian Compressive Sensing Approaches Research Articles

A Bayesian Compressive Sensing Approach to Robust Near-Field Antenna Characterization

A novel probabilistic sparsity-promoting method for robust near-field (NF) antenna characterization is proposed. It leverages on the measurements-by-design (MebD) paradigm and it exploits some a-priori information on the antenna under test (AUT) to generate an over-complete representation basis. Accordingly, the problem at hand is reformulated in a compressive sensing (CS) framework as the retrieval of a maximally-sparse distribution (with respect to the overcomplete basis) from a reduced set of measured data and then it is solved by means of a Bayesian strategy. Representative numerical results are presented to, also comparatively, assess the effectiveness of the proposed approach in reducing the "burden/cost" of the acquisition process as well as to mitigate (possible) truncation errors when dealing with space-constrained probing systems.

Multi-Frequency GPR Microwave Imaging of Sparse Targets through a Multi-Task Bayesian Compressive Sensing Approach.

An innovative inverse scattering (IS) method is proposed for the quantitative imaging of pixel-sparse scatterers buried within a lossy half-space. On the one hand, such an approach leverages on the wide-band nature of ground penetrating radar (GPR) data by jointly processing the multi-frequency (MF) spectral components of the collected radargrams. On the other hand, it enforces sparsity priors on the problem unknowns to yield regularized solutions of the fully non-linear scattering equations. Towards this end, a multi-task Bayesian compressive sensing (MT-BCS) methodology is adopted and suitably customized to take full advantage of the available frequency diversity and of the a-priori information on the class of imaged targets. Representative results are reported to assess the proposed MF-MT-BCS strategy also in comparison with competitive state-of-the-art alternatives.

Localization of Multiple RF Sources Based on Bayesian Compressive Sensing Using a Limited Number of UAVs With Airborne RSS Sensor

Locating multiple Radio Frequency (RF) sources by Unmanned Aerial Vehicles (UAVs) using Received Signal Strength (RSS) measurements attracts extensive attention for its intrinsic simplicity in hardware and low cost. However, the signals from multiple RF sources are aggregated at the airborne sensors, making it hard to achieve multi-emitter localization with a limited number of UAVs. In this paper, the Bayesian compressive sensing (BCS) approach is applied to estimating ground targets’ locations exploiting the RSS data collected by the onboard sensors. Meanwhile, to support the BCS approach, which requires sufficient measurements to achieve multi-emitter localization, a sensing architecture comprising a limited number of UAVs is presented. Besides, to optimize the localization result, we put forward a trajectory planning-based Bayesian compressive sensing (TPBCS) algorithm. This algorithm dynamically plans UAVs’ trajectories based on the adaptive simulated annealing (ASA) algorithm, which can bring down the computational cost in UAV path planning compared to the traditional simulated annealing (SA) algorithm. The effectiveness and robustness of the approach are validated by simulations in the end.

Full-Vectorial 3D Microwave Imaging of Sparse Scatterers through a Multi-Task Bayesian Compressive Sensing Approach.

In this paper, the full-vectorial three-dimensional (3D) microwave imaging (MI) of sparse scatterers is dealt with. Towards this end, the inverse scattering (IS) problem is formulated within the contrast source inversion (CSI) framework and it is aimed at retrieving the sparsest and most probable distribution of the contrast source within the imaged volume. A customized multi-task Bayesian compressive sensing (MT-BCS) method is used to yield regularized solutions of the 3D-IS problem with a remarkable computational efficiency. Selected numerical results on representative benchmarks are presented and discussed to assess the effectiveness and the reliability of the proposed MT-BCS strategy in comparison with other competitive state-of-the-art approaches, as well.

Planar Array Diagnostic Tool for Millimeter-Wave Wireless Communication Systems

In this paper, a diagnostic tool or procedure based on Bayesian compressive sensing (BCS) is proposed for identification of failed element(s) which manifest in millimeter-wave planar antenna arrays. With adequate a priori knowledge of the reference antenna array radiation pattern, a diagnostic problem of faulty elements was formulated. Sparse recovery algorithms, including total variation (TV), mixed ℓ 1 / ℓ 2 norm, and minimization of the ℓ 1 , are readily available in the literature, and were used to diagnose the array under test (AUT) from measurement points, consequently providing faster and better diagnostic schemes than the traditional mechanisms, such as the back propagation algorithm, matrix method algorithm, etc. However, these approaches exhibit some drawbacks in terms of effectiveness and reliability in noisy data, and a large number of measurement data points. To overcome these problems, a methodology based on BCS was adapted in this paper. From far-field radiation pattern samples, planar array diagnosis was formulated as a sparse signal recovery problem where BCS was applied to recover the locations of the faults using relevance vector machine (RVM). The resulted BCS approach was validated through simulations and experiments to provide suitable guidelines for users, as well as insight into the features and potential of the proposed procedure. A Ka-band ( 28.9 GHz ) 10 × 10 rectangular microstrip patch antenna array that emulates failure with zero excitation was designed for far-field measurements in an anechoic chamber. Both simulated and measured far-field samples were used to test the proposed approach. The proposed technique is demonstrated to detect diagnostic problems with fewer measurements provided the prior knowledge of the array radiation pattern is known, and the number of faults is relatively smaller than the array size. The effectiveness and reliability of the technique is verified experimentally and via simulation. In addition to a faster diagnosis and better reconstruction accuracy, the BCS-based technique shows more robustness to additive noisy data compared to other compressive sensing methods. The proposed procedure can be applied to next-generation transceivers, aerospace systems, radar systems, and other communication systems.

Bayesian Compressive Sensing Approaches for Direction of Arrival Estimation With Mutual Coupling Effects

The problem of estimating the dynamic direction of arrival of far field signals impinging on a uniform linear array, with mutual coupling effects, is addressed. This work proposes two novel approaches able to provide accurate solutions, including at the endfire regions of the array. Firstly, a Bayesian compressive sensing Kalman filter is developed, which accounts for the predicted estimated signals rather than using the traditional sparse prior. The posterior probability density function of the received source signals and the expression for the related marginal likelihood function are derived theoretically. Next, a Gibbs sampling based approach with indicator variables in the sparsity prior is developed. This allows sparsity to be explicitly enforced in different ways, including when an angle is too far from the previous estimate. The proposed approaches are validated and evaluated over different test scenarios and compared to the traditional relevance vector machine based method. An improved accuracy in terms of average root mean square error values is achieved (up to 73.39% for the modified relevance vector machine based approach and 86.36% for the Gibbs sampling based approach). The proposed approaches prove to be particularly useful for direction of arrival estimation when the angle of arrival moves into the endfire region of the array.

Achieving High-Quality Three-Dimensional InISAR Imageries of Maneuvering Target via Super-Resolution ISAR Imaging by Exploiting Sparseness

Interferometric inverse synthetic aperture radar (InISAR) can achieve 3-D imageries of maneuvering target. However, the quality of 3-D imageries suffers from the noise and sidelobes seriously. In addition, the defocusing effect due to the nonlinear moving of the target can also bring into a bad effect for 3-D imaging. To tackle these issues, a 3-D InISAR imaging approach via compressed sensing (CS)-based super-resolution (SR) ISAR imageries is proposed. First, we establish the target sparsity-constraint optimization model containing both amplitude and phase information of scatterers. Second, a Bayesian CS approach is adopted to get high-quality SR ISAR image. The quasi-Newton solver guarantees both high amplitude and phase information recovery precision. At last, a high-quality 3-D view of the target is achieved via the conventional ISAR imagery pair interferometry technique. Computer simulation and real data experimental results verify the effectiveness of the proposed method.

Bayesian Compressive Sensing Approaches for the Reconstruction of Two-Dimensional Sparse Scatterers Under TE Illuminations

In this paper, the reconstruction of sparse scatterers under multiview transverse-electric illumination is dealt with. Starting from a probabilistic formulation of the “inverse source” problem, two Bayesian compressive sensing approaches are introduced. The former is a suitable extension of the single-task method presented earlier for the transverse-magnetic scalar case, while the other exploits an innovative multitask implementation to take into account the relationships among the “contrast currents” at the different probing views. Representative numerical results are discussed to assess, also comparatively, the numerical efficiency, the accuracy, and the robustness of the proposed approaches.

Sparse scatterers imaging through approximated multitask compressive sensing strategies

AbstractIn the framework of the multitask Bayesian compressive sensing (BCS) paradigm, two innovative approaches for the solution of coherent imaging problems are proposed within the first‐order Born approximation and the Rytov approximation.Selected numerical results are reported to compare the proposed techniques with state‐of‐the‐art BCS approaches as well as global optimization‐based methods. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:1553–1558, 2013; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.27612

Reliable Diagnosis of Large Linear Arrays—A Bayesian Compressive Sensing Approach

An innovative array diagnosis technique based on a compressive-sensing (CS) paradigm is introduced in the case of linear arrangements. Besides detecting the faulty elements, the approach is able to provide the degree of reliability of such an estimation. Starting from the measured samples of the far-field pattern, the array diagnosis problem is formulated in a Bayesian framework and it is successively solved with a fast relevance vector machine (RVM). The arising Bayesian compressive sensing (BCS) approach is numerically validated through a set of representative examples aimed at providing suitable user's guidelines as well as some insights on the method features and potentialities.