The QUIET Instrument

The Q/U Imaging ExperimenT (QUIET) is designed to measure polarization in the Cosmic Microwave Background, targeting the imprint of inflationary gravitational waves at large angular scales (~ 1 degree). Between 2008 October and 2010 December, two independent receiver arrays were deployed sequentially on a 1.4 m side-fed Dragonian telescope. The polarimeters which form the focal planes use a highly compact design based on High Electron Mobility Transistors (HEMTs) that provides simultaneous measurements of the Stokes parameters Q, U, and I in a single module. The 17-element Q-band polarimeter array, with a central frequency of 43.1 GHz, has the best sensitivity (69 uK sqrt(s)) and the lowest instrumental systematic errors ever achieved in this band, contributing to the tensor-to-scalar ratio at r < 0.1. The 84-element W-band polarimeter array has a sensitivity of 87 uK sqrt(s) at a central frequency of 94.5 GHz. It has the lowest systematic errors to date, contributing at r < 0.01. The two arrays together cover multipoles in the range l= 25-975. These are the largest HEMT-based arrays deployed to date. This article describes the design, calibration, performance of, and sources of systematic error for the instrument

Robust beamforming with magnitude response constraints using iterative second-order cone programming

The problem of robust beamforming for antenna arrays with arbitrary geometry and magnitude response constraints is one of considerable importance. Due to the presence of the non-convex magnitude response constraints, conventional convex optimization techniques cannot be applied directly. A new approach based on iteratively linearizing the non-convex constraints is then proposed to reformulate the non-convex problem to a series of convex subproblems, each of which can be optimally solved using second-order cone programming (SOCP). Moreover, in order to obtain a more robust beamformer against array imperfections, the proposed method is further extended by optimizing its worst-case performance using again SOCP. Different from some conventional methods which are restricted to linear arrays, the proposed method is applicable to arbitrary array geometries since the weight vector, rather than its autocorrelation sequence, is used as the variable. Simulation results show that the performance of the proposed method is comparable to the optimal solution previously proposed for uniform linear arrays, and it also gives satisfactory results under different array specifications and geometries tested. © 2006 IEEE.published_or_final_versio

Data Analysis for the Microwave Anisotropy Probe (MAP) Mission

We present an overview of the upcoming Microwave Anisotropy Probe (MAP) mission, with an emphasis on those aspects of the mission that simplify the data analysis. The method used to make sky maps from the differential temperature data is reviewed and we present some of the noise properties expected from these maps. An overview of the method we plan to use to mine the angular power spectrum from the mega-pixel sky maps closes the paper.Comment: For the MAP Science Team. 11 pages, 5 figures, to appear in Mining the Sky, ESO Astrophysics Symposia Serie

Sparsity based methods for target localization in multi-sensor radar

In this dissertation, several sparsity-based methods for ground moving target indicator (GMTI) radar with multiple-input multiple-output (MIMO) random arrays are proposed. MIMO random arrays are large arrays that employ multiple transmitters and receivers, the positions of the transmitters and the receivers are randomly chosen. Since the resolution of the array depends on the size of the array, MIMO random arrays obtain a high resolution. However, since the positions of the sensors are randomly chosen, the array suffers from large sidelobes which may lead to an increased false alarm probability. The number of sensors of a MIMO random array required to maintain a certain level of peak sidelobes is studied. It is shown that the number of sensors scales with the logarithm of the array aperture, in contrast with a ULA where the number of elements scales linearly with the array aperture. The problem of sparse target detection given space-time observations from MIMO random arrays is presented. The observations are obtained in the presence of Gaussian colored noise of unknown covariance matrix, but for which secondary data is available for its estimation. To solve the detection problem two sparsity-based algorithms, the MP-STAP and the MBMP-STAP algorithms are proposed that utilizes knowledge of the upper bound on the number of targets. A constant false alarm rate (CFAR) sparsity based detector that does not utilize any information on the number of targets referred to as MP-CFAR and MBMP-CFAR are also developed. A performance analysis for the new CFAR detector is also derived, the metrics used to describe the performance of the detector are the probability of false alarm and the probability of detection. A grid refinement procedure is also proposed to eliminate the need for a dense grid which would increase the computational complexity significantly. Expressions for the computational complexity of the proposed CFAR detectors are derived. It is shown that the proposed CFAR detectors outperforms the popular adaptive beamformer at a modest increase in computational complexity

Maximizing the Number of Spatial Nulls with Minimum Sensors

In this paper, we attempt to unify two array processing frameworks viz, Acoustic Vector Sensor (AVS) and two level nested array to enhance the Degrees of Freedom (DoF) significantly beyond the limit that is attained by a Uniform Linear Hydrophone Array (ULA) with specified number of sensors. The major focus is to design a line array architecture which provides high resolution unambiguous bearing estimation with increased number of spatial nulls to mitigate the multiple interferences in a deep ocean scenario. AVS can provide more information about the propagating acoustic field intensity vector by simultaneously measuring the acoustic pressure along with tri-axial particle velocity components. In this work, we have developed Nested AVS array (NAVS) ocean data model to demonstrate the performance enhancement. Conventional and MVDR spatial filters are used as the response function to evaluate the performance of the proposed architecture. Simulation results show significant improvement in performance viz, increase of DoF, and localization of more number of acoustic sources and high resolution bearing estimation with reduced side lobe level

HI Epoch of Reionization Arrays

There are few data available with which to constrain the thermal history of the intergalactic medium (IGM) following global recombination. Thus far, most constraints flow from analyses of the Cosmic Microwave Background and optical spectroscopy along a few lines of sight. However, direct study of the IGM in emission or absorption against the CMB via the 1S hyperfine transition of Hydrogen would enable broad characterization thermal history and source populations. New generations of radio arrays are in development to measure this line signature. Bright foreground emission and the complexity of instrument calibration models are significant hurdles. How to optimize these is uncertain, resulting in a diversity in approaches. We discuss recent limits on line brightness, array efforts including the new Large Aperture Experiment to Detect the Dark Ages (LEDA), and the next generation Hydrogen Reionization Array (HERA) concept.Comment: 8 pages, 4 figures, 1 table. Invited review to the 11th Asian-Pacific Regional IAU Meeting 2011, NARIT Conference Series, Vol. 1 eds. S. Komonjinda, Y. Kovalev, and D. Ruffolo (2012