Adaptive Selective Sidelobe Canceller Beamformer With Applications to Interference Mitigation in Radio Astronomy

Levanda, Ronny; Leshem, Amir

doi:10.1109/tsp.2013.2274960

Cited by 18 publications

(6 citation statements)

References 40 publications

(75 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…and the beamformer that achieves this was called the adaptive selective sidelobe canceller (ASSC) in [17]. One problem with using this result in practice is that σ opt,i depends on a single snapshot.…”

Section: Bounds On the Imagementioning

confidence: 99%

See 1 more Smart Citation

Radio astronomical image formation using constrained least squares and Krylov subspaces

Sardarabadi

Leshem

Veen

2016

A&A

Self Cite

View full text Add to dashboard Cite

Aims. Image formation for radio astronomy can be defined as estimating the spatial intensity distribution of celestial sources throughout the sky, given an array of antennas. One of the challenges with image formation is that the problem becomes ill-posed as the number of pixels becomes large. The introduction of constraints that incorporate a priori knowledge is crucial. Methods. In this paper we show that in addition to non-negativity, the magnitude of each pixel in an image is also bounded from above. Indeed, the classical "dirty image" is an upper bound, but a much tighter upper bound can be formed from the data using array processing techniques. This formulates image formation as a least squares optimization problem with inequality constraints. We propose to solve this constrained least squares problem using active set techniques, and the steps needed to implement it are described. It is shown that the least squares part of the problem can be efficiently implemented with Krylov-subspace-based techniques. We also propose a method for correcting for the possible mismatch between source positions and the pixel grid. This correction improves both the detection of sources and their estimated intensities. The performance of these algorithms is evaluated using simulations. Results. Based on parametric modeling of the astronomical data, a new imaging algorithm based on convex optimization, active sets, and Krylov-subspace-based solvers is presented. The relation between the proposed algorithm and sequential source removing techniques is explained, and it gives a better mathematical framework for analyzing existing algorithms. We show that by using the structure of the algorithm, an efficient implementation that allows massive parallelism and storage reduction is feasible. Simulations are used to compare the new algorithm to classical CLEAN. Results illustrate that for a discrete point model, the proposed algorithm is capable of detecting the correct number of sources and producing highly accurate intensity estimates.

show abstract

Section: Bounds On the Imagementioning

confidence: 99%

“…One problem with using this result in practice is that σ opt,i depends on a single snapshot. This means that there is a variance-bias trade-off when we have a sample covariance matrix R instead of the true covariance matrix R. An analysis of this problem and various solutions for it are discussed in [17].…”

Section: Bounds On the Imagementioning

confidence: 99%

Radio astronomical image formation using constrained least squares and Krylov subspaces

Sardarabadi

Leshem

Veen

2016

A&A

Self Cite

View full text Add to dashboard Cite

show abstract

“…It will also provide the full implementation of the algorithm which is computationally simple but requires careful use of Krylov spaces to prevent the need to store very large matrices. Future work will show how to combine the proposed approach with the robust adaptive selective sidelobe canceler [27].…”

Section: Resultsmentioning

confidence: 99%

Computationally efficient radio astronomical image formation using constrained least squares and and the MVDR beamformer

Sardarabadi

Leshem

Veen

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

Linear image deconvolution for radio-astronomy isan ill-posed problem. For this reason a-priori knowledge is crucial for improving the performance of the deconvolution. In this paper we show that combining non-negativity constraints with an upper bound on the magnitude of each pixel in the image can significantly improve the image formation algorithm. We also show that the minimum variance distortionless response (MVDR) dirty image provides the tightest upper bound among all beamformers. We then show how LS-MVI image formation a;gorithm can be reformulated as a preconditioned weighted least squares algorithm. The resulting algorithm can be efficiently solved using the active-set method. The performance of the algorithm is demonstrated in simulation and compared with constrained least squares based on the classical dirty image.

show abstract

“…It is important to note that, limited by the beam width, the imaging resolution on the across-track inevitably decreases with increasing distance. Therefore, some high-resolution beamforming algorithms used on the across-track, such as minimum variance distortionless response (MVDR) and deconvolved beamforming, should be researched further but are not the focus of this paper [26,27].…”

Section: Simulationmentioning

confidence: 99%

Obtaining 3D High-Resolution Underwater Acoustic Images by Synthesizing Virtual Aperture on the 2D Transducer Array of Multibeam Echo Sounder

Wei

Zhou

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

In recent decades, imaging sonar has been the most widely employed remote sensing instruments in the field of underwater detection. The multibeam echo sounder (MBES) plays an important role in obtaining high-accuracy seabed topography. However, the resolution of the MBES substantially decreases with the increasing distance. Synthetic aperture sonar (SAS) achieves constant resolution on the along-track, improving the fineness of the image. However, conventional side-scan SAS usually only achieves 2D images, and gaps always exist. In this modeling and experimental research paper, we propose a novel underwater acoustic imaging scheme to improve the imaging performance of MBES, based on the complementarity of MBES and SAS systems. We design a 2D transducer array to increase the detection efficiency and obtain spatial gain. Moreover, the processing scheme is analyzed to design the working parameters in actual engineering applications. We exploit a target echo simulation approach to establish the research basics of the imaging algorithms, which also reflects the shapes and shadows of targets to match actual situations as realistically as possible. The proposed imaging algorithm synthesizes a virtual aperture receiving array on the along-track and reserves the multi-element manifold on the across-track. This helps to improve the imaging quality of the MBES and achieves high-resolution 3D detection with no gaps. Simulation and tank experimental results demonstrate that the proposed scheme can significantly improve the detection ability of the MBES, especially for small 3D target detection, thus making it suitable for 3D high-resolution underwater detection applications.

show abstract

Adaptive Selective Sidelobe Canceller Beamformer With Applications to Interference Mitigation in Radio Astronomy

Cited by 18 publications

References 40 publications

Radio astronomical image formation using constrained least squares and Krylov subspaces

Radio astronomical image formation using constrained least squares and Krylov subspaces

Computationally efficient radio astronomical image formation using constrained least squares and and the MVDR beamformer

Obtaining 3D High-Resolution Underwater Acoustic Images by Synthesizing Virtual Aperture on the 2D Transducer Array of Multibeam Echo Sounder

Contact Info

Product

Resources

About