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Radio Astronomical Image Formation Using Constrained Least Squares and Krylov Subspaces

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Radio Astronomical Image Formation Using Constrained Least Squares and Krylov Subspaces A&A 588, A95 (2016) Astronomy DOI: 10.1051/0004-6361/201526214 & c ESO 2016 Astrophysics Radio astronomical image formation using constrained least squares and Krylov subspaces Ahmad Mouri Sardarabadi1, Amir Leshem2, and Alle-Jan van der Veen1 1 Department of electrical engineering, Delft University of Technology, PO Box 5031, 2600 GA Delft, The Netherlands e-mail: [email protected] 2 Faculty of Engineering, Bar-Ilan University, Aharon and Rachel Dahan Electronic Technology Buildilng, Ramat Gan, Israël Received 27 March 2015 / Accepted 27 October 2015 ABSTRACT Aims. Image formation for radio astronomy can be defined as estimating the spatial intensity distribution of celestial sources through- out 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. Key words. techniques: image processing – instrumentation: interferometers – methods: numerical 1. Introduction approaches described in Cornwell (2008), Rau et al. (2009), Bhatnager & Cornwell (2004). Image formation for radio astronomy can be defined as estimat- ing the spatial intensity distribution of celestial sources over the The conditioning of the image deconvolution step can be im- sky. The measurement equation (“data model”) is linear in the proved by incorporating side information such as non-negativity source intensities, and the resulting least squares problem has of the image (Briggs 1995), source model structure beyond sim- classically been implemented in two steps: formation of a “dirty ple point sources (e.g., shapelets and wavelets, Reid 2006), spar- image”, followed by a deconvolution step. In this process, an sity or 1 constraints on the image (Levanda & Leshem 2008; implicit model assumption is made that the number of sources Wiaux et al. 2009), or a combination of both wavelets and spar- is discrete, and subsequently the number of sources has been re- sity (Carrillo et al. 2012, 2014). Beyond these, some fundamen- placed by the number of image pixels (assuming each pixel may tal approaches based on parameter estimation techniques have contain a source). been proposed, such as the least squares minimum variance imaging (LS-MVI) (Ben-David & Leshem 2008), maximum- The deconvolution step becomes ill-conditioned if the num- likelihood-based techniques (Leshem & van der Veen 2000), and berofpixelsislarge(Wijnholds & van der Veen 2008). Bayesian-based techniques (Junklewitz et al. 2016; Lochner Alternatively, the directions of sources may be estimated along et al. 2015). Computational complexity is a concern that has not with their intensities, but this is a complex nonlinear problem. been addressed in these approaches. Classically, this has been implemented as an iterative subtrac- tion technique, wherein source directions are estimated from the New radio telescopes such as the Low Frequency Array dirty image, and their contribution is subtracted from the data. (LOFAR), the Allen Telescope Array (ATA), Murchison This mixed approach is the essence of the CLEAN method pro- Widefield Array (MWA), and the Long Wavelength Array posed by Högbom (Högbom 1974), which was subsequently re- (LWA) are composed of many stations (each station made up of fined and extended in several ways, leading to the widely used multiple antennas that are combined using adaptive beamform- ing), and the increase in number of antennas and stations con- tinues in the design of the square kilometer array (SKA). These This research was supported by NWO-TOP 2010, 614.00.005. The instruments have or will have a significantly increased sensitiv- research of A. Leshem was supported by the Israeli Science foundation, ity and a larger field of view compared to traditional telescopes, grant 1240−2009. leading to many more sources that need to be considered. They Article published by EDP Sciences A95, page 1 of 19 A&A 588, A95 (2016) also need to process larger bandwidths to reach this sensitivity. A calligraphic capital letter such as X represents a set of in- Besides the increased requirements on the performance of imag- dices, and aX is a column vector constructed by stacking the ing, the improved spatial resolution leads to an increasing num- elements of a that belong to X. The corresponding indices are ber of pixels in the image, and the development of computation- stored with the vector as well (similar to the storage of matlab ally efficient techniques is critical. “sparse” vectors). To benefit from the vast literature related to solving least The operator vect(·) stacks the columns of the argument ma- squares problems, but also to gain from the nonlinear process- trix to form a vector, vectdiag(·) stacks the diagonal elements of ing offered by standard deconvolution techniques, we propose the argument matrix to form a vector, and diag(·) is a diagonal to reformulate the imaging problem as a parameter-estimation matrix with its diagonal entries from the argument vector (if the problem described by a weighted least squares optimization argument is a matrix diag(·) = diag(vectdiag(·))). problem with several constraints. The first is a non-negativity Let ⊗ denote the Kronecker product, i.e., constraint, which would lead to the non-negative least squares ⎡ ⎤ ⎢ a11B a12B ···⎥ algorithm (NNLS) proposed in Briggs (1995). But we show that ⎢ ···⎥ the pixel values are also bounded from above. A coarse up- A ⊗ B := ⎢ a21B a22B ⎥ . ⎣⎢ . ⎦⎥ per bound is provided by the classical dirty image, and a much . .. tighter bound is the “minimum variance distortionless response” (MVDR) dirty image that was proposed in the context of radio Furthermore, ◦ denotes the Khatri-Rao product (column-wise astronomy in Leshem & van der Veen (2000). Kronecker product), i.e., We propose to solve the resulting constrained least squares A ◦ B := [a ⊗ b , a ⊗ b , ···], problems using an active set approach. This results in a compu- 1 1 2 2 tationally efficient imaging algorithm that is closely related to and denotes the Schur-Hadamard (elementwise) product. The existing nonlinear sequential source estimation techniques such following properties are used throughout the paper (for matrices as CLEAN with the benefit of accelerated convergence thanks and vectors with compatible dimensions): to tighter upper bounds on the intensity over the complete im- T ⊗ = age. Because the constraints are enforced over the entire image, (B A)vect(X) vect(AXB)(1) this eliminates the inclusion of negative flux sources and other (B ⊗ A)H = (BH ⊗ AH)(2) anomalies that appear in some existing sequential techniques. ⊗ −1 = −1 ⊗ −1 To reduce the computational complexity further, we show (B A) (B A )(3) that the data model has a Khatri-Rao structure. This can be ex- (BT ◦ A)x = vect(Adiag(x)B)(4) ploited to significantly improve the data management and par- (BC ⊗ AD) = (B ⊗ A)(C ⊗ D)(5) allelism compared to general implementations of least squares ◦ = ⊗ ◦ algorithms. (BC AD) (B A)(C D)(6) The structure of the paper is as follows. In Sect. 2 we de- (BHC AHD) = (B ◦ A)H(C ◦ D)(7) scribe the basic data model and the image formation problem ∗ vectdiag(AHXA) = (A ◦ A)Hvect(X). (8) in Sect. 3. A constrained least squares problem is formulated, using various intensity constraints that take the form of dirty images. The solution of this problem using active set tech- 2. Data model niques in Sect. 4 generalizes the classical CLEAN algorithm. In Sect. 5 we discuss the efficient implementation of a key step We consider an instrument where P receivers (stations or anten- in the active set solution using Krylov subspaces. We end up nas) observe the sky. Assuming a discrete point source model, with some simulated experiments that demonstrate the advan- we let Q denote the number of visible sources. The received tages of the proposed technique and conclusions regarding fu- signals at the antennas are sampled and subsequently split into ture implementation. narrow sub-bands. For simplicity, we consider only a single sub- band in the rest of the paper. Although the sources are consid- ered stationary, the apparent position of the celestial sources will Notation change with time because of the earth’s rotation. For this reason A boldface letter such as a denotes a column vector, a boldface the data is split into short blocks or “snapshots” of N samples, where the exact value of N depends on the resolution of the in- capital letter such as A denotes a matrix.
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