Multisource Self-calibration for Sensor Arrays

Calibration of a sensor array is more involved if the antennas have direction dependent gains and multiple calibrator sources are simultaneously present. We study this case for a sensor array with arbitrary geometry but identical elements, i.e. elements with the same direction dependent gain pattern. A weighted alternating least squares (WALS) algorithm is derived that iteratively solves for the direction independent complex gains of the array elements, their noise powers and their gains in the direction of the calibrator sources. An extension of the problem is the case where the apparent calibrator source locations are unknown, e.g., due to refractive propagation paths. For this case, the WALS method is supplemented with weighted subspace fitting (WSF) direction finding techniques. Using Monte Carlo simulations we demonstrate that both methods are asymptotically statistically efficient and converge within two iterations even in cases of low SNR.Comment: 11 pages, 8 figure

Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees

This paper addresses the problem of ad hoc microphone array calibration where only partial information about the distances between microphones is available. We construct a matrix consisting of the pairwise distances and propose to estimate the missing entries based on a novel Euclidean distance matrix completion algorithm by alternative low-rank matrix completion and projection onto the Euclidean distance space. This approach confines the recovered matrix to the EDM cone at each iteration of the matrix completion algorithm. The theoretical guarantees of the calibration performance are obtained considering the random and locally structured missing entries as well as the measurement noise on the known distances. This study elucidates the links between the calibration error and the number of microphones along with the noise level and the ratio of missing distances. Thorough experiments on real data recordings and simulated setups are conducted to demonstrate these theoretical insights. A significant improvement is achieved by the proposed Euclidean distance matrix completion algorithm over the state-of-the-art techniques for ad hoc microphone array calibration.Comment: In Press, available online, August 1, 2014. http://www.sciencedirect.com/science/article/pii/S0165168414003508, Signal Processing, 201

Results from the Palo Verde neutrino oscillation experiment

The ν̅e flux and spectrum have been measured at a distance of about 800 m from the reactors of the Palo Verde Nuclear Generating Station using a segmented Gd-loaded liquid scintillator detector. Correlated positron-neutron events from the reaction ν̅ep→e+n were recorded for a period of 200 d including 55 d with one of the three reactors off for refueling. Backgrounds were accounted for by making use of the reactor-on and reactor-off cycles, and also with a novel technique based on the difference between signal and background under reversal of the e+ and n portions of the events. A detailed description of the detector calibration, background subtraction, and data analysis is presented here. Results from the experiment show no evidence for neutrino oscillations. ν̅e→ν̅x oscillations were excluded at 90% C.L. for Δm2>1.12×10-3 eV2 for full mixing and sin22θ>0.21 for large Δm2. These results support the conclusion that the observed atmospheric neutrino oscillations do not involve νe

On the detection of spectral ripples from the Recombination Epoch

Photons emitted during the epochs of Hydrogen ($500 \lesssim z \lesssim 1600$) and Helium recombination ($1600 \lesssim z \lesssim 3500$ for HeII $\rightarrow$ HeI, $5000 \lesssim z \lesssim 8000$ for HeIII $\rightarrow$ HeII) are predicted to appear as broad, weak spectral distortions of the Cosmic Microwave Background. We present a feasibility study for a ground-based experimental detection of these recombination lines, which would provide an observational constraint on the thermal ionization history of the Universe, uniquely probing astrophysical cosmology beyond the last scattering surface. We find that an octave band in the 2--6 GHz window is optimal for such an experiment, both maximizing signal-to-noise ratio and including sufficient line spectral structure. At these frequencies the predicted signal appears as an additive quasi-sinusoidal component with amplitude about $8$ nK that is embedded in a sky spectrum some nine orders of magnitude brighter. We discuss an algorithm to detect these tiny spectral fluctuations in the sky spectrum by foreground modeling. We introduce a \textit{Maximally Smooth} function capable of describing the foreground spectrum and distinguishing the signal of interest. With Bayesian statistical tests and mock data we estimate that a detection of the predicted distortions is possible with 90\% confidence by observing for 255 days with an array of 128 radiometers using cryogenically cooled state-of-the-art receivers. We conclude that detection is in principle feasible in realistic observing times; we propose APSERa---Array of Precision Spectrometers for the Epoch of Recombination---a dedicated radio telescope to detect these recombination lines.Comment: 33 pages, 16 figures, submitted to ApJ, comments welcom

Radio-Optical Galaxy Shape Correlations in the COSMOS Field

We investigate the correlations in galaxy shapes between optical and radio wavelengths using archival observations of the COSMOS field. Cross-correlation studies between different wavebands will become increasingly important for precision cosmology as future large surveys may be dominated by systematic rather than statistical errors. In the case of weak lensing, galaxy shapes must be measured to extraordinary accuracy (shear systematics of $< 0.01\%$) in order to achieve good constraints on dark energy parameters. By using shape information from overlapping surveys in optical and radio bands, robustness to systematics may be significantly improved without loss of constraining power. Here we use HST-ACS optical data, VLA radio data, and extensive simulations to investigate both our ability to make precision measurements of source shapes from realistic radio data, and to constrain the intrinsic astrophysical scatter between the shapes of galaxies as measured in the optical and radio wavebands. By producing a new image from the VLA-COSMOS L-band radio visibility data that is well suited to galaxy shape measurements, we are able to extract precise measurements of galaxy position angles. Comparing to corresponding measurements from the HST optical image, we set a lower limit on the intrinsic astrophysical scatter in position angles, between the optical and radio bands, of $\sigma_\alpha > 0.212\pi$ radians (or $38.2^{\circ}$) at a $95\%$ confidence level.Comment: 17 pages, 13 figure, 5 tables. Updated to match published version with a number of typographical correction

Robust Bayesian target detection algorithm for depth imaging from sparse single-photon data

This paper presents a new Bayesian model and associated algorithm for depth and intensity profiling using full waveforms from time-correlated single-photon counting (TCSPC) measurements in the limit of very low photon counts (i.e., typically less than 20 photons per pixel). The model represents each Lidar waveform as an unknown constant background level, which is combined in the presence of a target, to a known impulse response weighted by the target intensity and finally corrupted by Poisson noise. The joint target detection and depth imaging problem is expressed as a pixel-wise model selection and estimation problem which is solved using Bayesian inference. Prior knowledge about the problem is embedded in a hierarchical model that describes the dependence structure between the model parameters while accounting for their constraints. In particular, Markov random fields (MRFs) are used to model the joint distribution of the background levels and of the target presence labels, which are both expected to exhibit significant spatial correlations. An adaptive Markov chain Monte Carlo algorithm including reversible-jump updates is then proposed to compute the Bayesian estimates of interest. This algorithm is equipped with a stochastic optimization adaptation mechanism that automatically adjusts the parameters of the MRFs by maximum marginal likelihood estimation. Finally, the benefits of the proposed methodology are demonstrated through a series of experiments using real data.Comment: arXiv admin note: text overlap with arXiv:1507.0251

Radio astronomical imaging in the presence of strong radio interference

Radio-astronomical observations are increasingly contaminated by interference, and suppression techniques become essential. A powerful candidate for interference mitigation is adaptive spatial filtering. We study the effect of spatial filtering techniques on radio astronomical imaging. Current deconvolution procedures such as CLEAN are shown to be unsuitable to spatially filtered data, and the necessary corrections are derived. To that end, we reformulate the imaging (deconvolution/calibration) process as a sequential estimation of the locations of astronomical sources. This not only leads to an extended CLEAN algorithm, the formulation also allows to insert other array signal processing techniques for direction finding, and gives estimates of the expected image quality and the amount of interference suppression that can be achieved. Finally, a maximum likelihood procedure for the imaging is derived, and an approximate ML image formation technique is proposed to overcome the computational burden involved. Some of the effects of the new algorithms are shown in simulated images. Keywords: Radio astronomy, synthesis imaging, parametric imaging, interference mitigation, spatial filtering, maximum likelihood, minimum variance, CLEAN.Comment: 27 pages, 7 figures. Paper with higher resolution color figures at http://cobalt.et.tudelft.nl/~leshem/postscripts/leshem/imaging.ps.g