Distributed Cooperative Localization in Wireless Sensor Networks without NLOS Identification

In this paper, a 2-stage robust distributed algorithm is proposed for cooperative sensor network localization using time of arrival (TOA) data without identification of non-line of sight (NLOS) links. In the first stage, to overcome the effect of outliers, a convex relaxation of the Huber loss function is applied so that by using iterative optimization techniques, good estimates of the true sensor locations can be obtained. In the second stage, the original (non-relaxed) Huber cost function is further optimized to obtain refined location estimates based on those obtained in the first stage. In both stages, a simple gradient descent technique is used to carry out the optimization. Through simulations and real data analysis, it is shown that the proposed convex relaxation generally achieves a lower root mean squared error (RMSE) compared to other convex relaxation techniques in the literature. Also by doing the second stage, the position estimates are improved and we can achieve an RMSE close to that of the other distributed algorithms which know \textit{a priori} which links are in NLOS.Comment: Accepted in WPNC 201

Fast and robust anchor calibration in range-based wireless localization

In this paper we investigate the anchor calibration problem where we want to find the anchor positions when the anchors are not able to range between each other. This is a problem of practical interest because in many systems, the anchors are not connected in a network but are just simple responders to range requests. The proposed calibration method is designed to be fast and simple using only a single range-capable device. For the estimation of the inter-anchor distances, we propose a Total Least Squares estimator as well as a L1 norm estimator. Real life experiments using publicly available hardware validate the proposed calibration technique and show the robustness of the algorithm to non-line-of-sight measurements

How Common are the Magellanic Clouds?

We introduce a probabilistic approach to the problem of counting dwarf satellites around host galaxies in databases with limited redshift information. This technique is used to investigate the occurrence of satellites with luminosities similar to the Magellanic Clouds around hosts with properties similar to the Milky Way in the object catalog of the Sloan Digital Sky Survey. Our analysis uses data from SDSS Data Release 7, selecting candidate Milky-Way-like hosts from the spectroscopic catalog and candidate analogs of the Magellanic Clouds from the photometric catalog. Our principal result is the probability for a Milky-Way-like galaxy to host N_{sat} close satellites with luminosities similar to the Magellanic Clouds. We find that 81 percent of galaxies like the Milky Way are have no such satellites within a radius of 150 kpc, 11 percent have one, and only 3.5 percent of hosts have two. The probabilities are robust to changes in host and satellite selection criteria, background-estimation technique, and survey depth. These results demonstrate that the Milky Way has significantly more satellites than a typical galaxy of its luminosity; this fact is useful for understanding the larger cosmological context of our home galaxy.Comment: Updated to match published version. Added referenc

Mass estimation in the outer regions of galaxy clusters

We present a technique for estimating the mass in the outskirts of galaxy clusters where the usual assumption of dynamical equilibrium is not valid. The method assumes that clusters form through hierarchical clustering and requires only galaxy redshifts and positions on the sky. We apply the method to dissipationless cosmological N-body simulations where galaxies form and evolve according to semi-analytic modelling. The method recovers the actual cluster mass profile within a factor of two to several megaparsecs from the cluster centre. This error originates from projection effects, sparse sampling, and contamination by foreground and background galaxies. In the absence of velocity biases, this method can provide an estimate of the mass-to-light ratio on scales ~1-10 Mpc/h where this quantity is still poorly known.Comment: 14 pages, 7 figures, MN LaTeX style, MNRAS, in pres

Matched direction detectors and estimators for array processing with subspace steering vector uncertainties

In this paper, we consider the problem of estimating and detecting a signal whose associated spatial signature is known to lie in a given linear subspace but whose coordinates in this subspace are otherwise unknown, in the presence of subspace interference and broad-band noise. This situation arises when, on one hand, there exist uncertainties about the steering vector but, on the other hand, some knowledge about the steering vector errors is available. First, we derive the maximum-likelihood estimator (MLE) for the problem and compute the corresponding Cramer-Rao bound. Next, the maximum-likelihood estimates are used to derive a generalized likelihood ratio test (GLRT). The GLRT is compared and contrasted with the standard matched subspace detectors. The performances of the estimators and detectors are illustrated by means of numerical simulations