Gravitational Wave Burst Source Direction Estimation using Time and Amplitude Information

In this article we study two problems that arise when using timing and amplitude estimates from a network of interferometers (IFOs) to evaluate the direction of an incident gravitational wave burst (GWB). First, we discuss an angular bias in the least squares timing-based approach that becomes increasingly relevant for moderate to low signal-to-noise ratios. We show how estimates of the arrival time uncertainties in each detector can be used to correct this bias. We also introduce a stand alone parameter estimation algorithm that can improve the arrival time estimation and provide root-sum-squared strain amplitude (hrss) values for each site. In the second part of the paper we discuss how to resolve the directional ambiguity that arises from observations in three non co-located interferometers between the true source location and its mirror image across the plane containing the detectors. We introduce a new, exact relationship among the hrss values at the three sites that, for sufficiently large signal amplitudes, determines the true source direction regardless of whether or not the signal is linearly polarized. Both the algorithm estimating arrival times, arrival time uncertainties, and hrss values and the directional follow-up can be applied to any set of gravitational wave candidates observed in a network of three non co-located interferometers. As a case study we test the methods on simulated waveforms embedded in simulations of the noise of the LIGO and Virgo detectors at design sensitivity.Comment: 10 pages, 14 figures, submitted to PR

Map-Aware Models for Indoor Wireless Localization Systems: An Experimental Study

The accuracy of indoor wireless localization systems can be substantially enhanced by map-awareness, i.e., by the knowledge of the map of the environment in which localization signals are acquired. In fact, this knowledge can be exploited to cancel out, at least to some extent, the signal degradation due to propagation through physical obstructions, i.e., to the so called non-line-of-sight bias. This result can be achieved by developing novel localization techniques that rely on proper map-aware statistical modelling of the measurements they process. In this manuscript a unified statistical model for the measurements acquired in map-aware localization systems based on time-of-arrival and received signal strength techniques is developed and its experimental validation is illustrated. Finally, the accuracy of the proposed map-aware model is assessed and compared with that offered by its map-unaware counterparts. Our numerical results show that, when the quality of acquired measurements is poor, map-aware modelling can enhance localization accuracy by up to 110% in certain scenarios.Comment: 13 pages, 11 figures, 1 table. IEEE Transactions on Wireless Communications, 201

Array signal processing for maximum likelihood direction-of-arrival estimation

Emitter Direction-of-Arrival (DOA) estimation is a fundamental problem in a variety of applications including radar, sonar, and wireless communications. The research has received considerable attention in literature and numerous methods have been proposed. Maximum Likelihood (ML) is a nearly optimal technique producing superior estimates compared to other methods especially in unfavourable conditions, and thus is of significant practical interest. This paper discusses in details the techniques for ML DOA estimation in either white Gaussian noise or unknown noise environment. Their performances are analysed and compared, and evaluated against the theoretical lower bounds