Image formation in radio astronomy is a large-scale inverse problem that is inherently illposed. We present a general algorithmic framework based on a Bayesian-inspired regularized maximum likelihood formulation of the radio astronomical imaging problem with a focus on diffuse emission recovery from limited noisy correlation data. The algorithm is dubbed PRIor-conditioned Fast Iterative Radio Astronomy and is based on a direct embodiment of the regularization operator into the system by right preconditioning. The resulting system is then solved using an iterative method based on projections onto Krylov subspaces. We motivate the use of a beam-formed image (which includes the classical 'dirty image') as an efficient prior-conditioner. Iterative reweighting schemes generalize the algorithmic framework and can account for different regularization operators that encourage sparsity of the solution. The performance of the proposed method is evaluated based on simulated 1D and 2D array arrangements as well as actual data from the core stations of the Low Frequency Array radio telescope antenna configuration, and compared to state-of-the-art imaging techniques. We show the generality of the proposed method in terms of regularization schemes while maintaining a competitive reconstruction quality with the current reconstruction techniques. Furthermore, we show that exploiting Krylov subspace methods together with the proper noise-based stopping criteria results in a great improvement in imaging efficiency.
Image formation using the data from an array of sensors is a familiar problem in many fields such as radio astronomy, biomedical and geodetic imaging. The problem can be formulated as a least squares (LS) estimation problem and becomes ill-posed at high resolutions, i.e. large number of image pixels. In this paper we propose two regularization methods, one based on weighted truncation of the eigenvalue decomposition of the image deconvolution matrix and the other based on the prior knowledge of the "dirty image" using the available data. The methods are evaluated by simulations as well as actual data from a phased-array radio telescope in the Netherlands, the Low Frequency Array Radio Telescope (LOFAR).
Abstract-Providing automated granular control of lighting, along with user-driven control, results in an energy-efficient smart lighting system design while catering to personal occupant preferences. Two functional ingredients in such a system are: 1) sensing that provides granular information on occupant location and 2) a communication system to transmit control messages from a user. In this paper, we consider an ultrasonic circular array sensor that provides the dual functionality of granular occupant sensing and a communication receiver for user control transmissions. A ceiling-mounted sensor configuration with a colocated ultrasonic transmitter and array receiver is considered. To perform presence sensing, this transmitter sends periodic bursts of sinusoidal pulses that, upon reflection from the environment, are received at the array sensor. The echoes are processed to obtain estimates of range, azimuth, and elevation angles corresponding to possible occupant movements. A Kalman filter based on a near constant velocity model is used for target tracking. The resulting occupant location is used for energy-efficient lighting control. A user may in addition control lamps in its vicinity by sending messages at ultrasonic frequency, which are processed by the receiver array, and used to further adapt requested parameters of the lighting system. The proposed sensing and messaging solution is tested in an indoor office space with an eight-element receiver array sensor prototype.
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