Signal Processing for Phased Array Feeds in Radio Astronomical Telescopes

Jeffs, Brian D.; Warnick, Karl F.; Landon, Jonathan; Waldron, Jacob; Jones, David B.; Fisher, J. Richard; Norrod, R.

doi:10.1109/jstsp.2008.2005023

Cited by 92 publications

(60 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the interference-plus-noise covariance matrix, we can consider the output of the array in the absence of the signal of interest and collect a long-term sample covariance estimate for R i+n [3].…”

Section: Recursive Implementation Of the Bayesian Beamformermentioning

confidence: 99%

See 1 more Smart Citation

A recursive Bayesian beamforming for steering vector uncertainties

Han

Zhang

2013

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

A recursive Bayesian approach to narrowband beamforming for an uncertain steering vector of interest signal is presented. In this paper, the interference-plus-noise covariance matrix and signal power are assumed to be known. The steering vector is modeled as a complex Gaussian random vector that characterizes the level of steering vector uncertainty. Applying the Bayesian model, a recursive algorithm for minimum mean square error (MMSE) estimation is developed. It can be viewed as a mixture of conditional MMSE estimates weighted by the posterior probability density function of the random steering vector given the observed data. The proposed recursive Bayesian beamformer can make use of the information about the steering vector brought by all the observed data until the current short-term integration window and can estimate the mean and covariance of the steering vector recursively. Numerical simulations show that the proposed beamformer with the known signal power and interference-plus-noise covariance matrix outperforms the linearly constrained minimum variance, subspace projection, and other three Bayesian beamformers. After convergence, it has similar performance to the optimal Max-SINR beamformer with the true steering vector.

show abstract

Section: Recursive Implementation Of the Bayesian Beamformermentioning

confidence: 99%

“…It has applications in fields, such as radar, sonar, radio astronomy, speech processing, and wireless communications. [1][2][3][4][5]. It is well known, however, that the digital beamformers are sensitive to error in the estimated signal steering vector.…”

Section: Introductionmentioning

confidence: 99%

A recursive Bayesian beamforming for steering vector uncertainties

Han

Zhang

2013

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…The compound beam is the electronically formed beam produced by illuminating the dish by the FPA. The FPA concept is currently under study in The Netherlands [15], United States [16], [17] and Australia [18] as part of the technology road map towards the Square Kilometer Array (SKA) [19].…”

Section: Telescope Architecturesmentioning

confidence: 99%

“…This step allows proper weighting of the receiver paths to optimize the signal-to-noise ratio of the actual observation. An excellent overview of FPA signal processing is provided in [17].…”

Section: Compound Element Calibrationmentioning

confidence: 99%

Calibration challenges for future radio telescopes

Wijnholds

Tol

Nijboer

et al. 2010

IEEE Signal Process. Mag.

View full text Add to dashboard Cite

“…This collaboration has developed a detailed model for the PAF system and the associated beamforming algorithms [9,10]. The PAF modeling efforts of BYU led to a design procedure that will iteratively reshape the dipole structure in order to optimize the sensitivity of the array feed on a reflector telescope [11].…”

Section: Introductionmentioning

confidence: 99%

A highly-sensitive cryogenic phased array feed for the Green Bank Telescope

Roshi

Shillue

Fisher

et al. 2017

2017 XXXIInd General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS)

View full text Add to dashboard Cite

In this paper, we describe the development of a new L-band (1.4 GHz) Cryogenic Phased Array Feed (PAF) system, referred to as the GBT2 array. Results from initial measurements made with the GBT2 array are also presented. The PAF was developed for the Green Bank Telescope (GBT) as part of the Focal L-band Array for the GBT (FLAG) project. During the first stage of the development work (Phase I), a prototype cryogenic 19 element dual-polarized array with "Kite" dipole elements was developed and tested on the GBT. The measured system temperature over efficiency (T sys /η) ratio for the bore sight beam of the Kite array was 45.5 K at 1.55 GHz. The off-boresight T sys /η shows an increase by 13 K at an offset equal to the half power beam width (7 .2 at 1.7 GHz). Our measurements indicate that the off-boresight degradation and field-of-view (FoV) limitation of the Kite array is simply due to the fixed array size. To increase the FoV, a new 19-element GBT2 array with larger array spacing was developed during FLAG Phase II. The frequency response of the array was optimized from 1.2 to 1.6 GHz. A system with larger cryostat, new low noise amplifiers (LNAs), down-conversion and digitization close to the front end, unformatted digital transmission over fiber, ROACH II based polyphase filter banks (PFBs) with bandwidth 150 MHz and a data acquisition system that records voltage samples from one of the PFB channels were all developed. The data presented here is processed off-line. The receiver temperature measured (off the telescope, on cold sky/hot load, with no beamforming) with the new system is 17 K at 1.4 GHz, an improvement > 8 K over the previous Kite array. Measurements with the GBT2 array on the telescope are in progress. A real time 150 MHz beamformer is also being developed as part of an NSF-funded collaboration between NRAO/GBO/BYU & West Virginia University (Beamformer Project) to support science observations.

show abstract

Signal Processing for Phased Array Feeds in Radio Astronomical Telescopes

Cited by 92 publications

References 35 publications

A recursive Bayesian beamforming for steering vector uncertainties

A recursive Bayesian beamforming for steering vector uncertainties

Calibration challenges for future radio telescopes

A highly-sensitive cryogenic phased array feed for the Green Bank Telescope

Contact Info

Product

Resources

About