Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1304.6237

Self-Localization of Asynchronous Wireless Nodes With Parameter Uncertainties

Dave Zachariah·Alessio De Angelis·Satyam Dwivedi·Peter Händel
View original source ↗Read source document ↗Export metadata ↓

Abstract

We investigate a wireless network localization scenario in which the need for synchronized nodes is avoided. It consists of a set of fixed anchor nodes transmitting according to a given sequence and a self-localizing receiver node. The setup can accommodate additional nodes with unknown positions participating in the sequence. We propose a localization method which is robust with respect to uncertainty of the anchor positions and other system parameters. Further, we investigate the Cramér-Rao bound for the considered problem and show through numerical simulations that the proposed method attains the bound.

Explore related subjects

math.STcs.ITcs.NImath.ITstat.TH

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dave Zachariah, Alessio De Angelis, Satyam Dwivedi, Peter Händel. 2013-04-23. Self-Localization of Asynchronous Wireless Nodes With Parameter Uncertainties. https://doi.org/10.1109/lsp.2013.2255592

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Convergence Analysis of a Greedy Algorithm for Conditioning Gaussian Random Variables

In the context of Gaussian conditioning, greedy algorithms iteratively select the most informative measurements, given an observed Gaussian random variable. However, the convergence analysis for conditioning Gaussian random variables remains an open problem. We adress this by introducing an operator $M$ that allows us to transfer convergence rates of the observed Gaussian random variable approximation onto the conditional Gaussian random variable. Furthermore we apply greedy methods from approximation theory to obtain convergence rates. These greedy methods have already demonstrated optimal convergence rates within the setting of kernel based function approximation. In this paper, we establish an upper bound on the convergence rates concerning the norm of the approximation error of the conditional covariance operator.

math.ST↗
arXiv2026

Signal-to-Noise Ratio Inference for Multivariate High-Dimensional Linear Models

We study inference for the fraction of total response variation explained by multivariate high-dimensional linear models. Two moment equations yield explicit estimates without fitting the regression coefficients. We establish asymptotically valid confidence intervals for fixed- and random-effects models, allowing the response dimension to grow with the sample size. The theory describes how response dependence affects precision and provides a correction for unequal noise levels in the random-design setting. Simulations assess finite-sample performance, and an analysis of yeast growth across environments illustrates joint inference for additive marker-based signal.

math.ST↗
arXiv2026

Human-Anchored Inference for Ranking New Models with Large Language Model Judges

Human pairwise comparisons provide a reference for evaluating large language models (LLMs), but collecting sufficient judgments for each new release is costly and time-consuming. LLM judges offer a scalable alternative, although their comparisons may differ systematically from human preferences and across judges. We study the ranking of a new model that has received LLM-judge comparisons but no human comparisons. We propose ANCHOR (ANchored Comparisons for Human-reference inference with Orthogonal Riesz correction), which uses historical human and LLM comparisons to learn judge-specific sensitivities to human score differences and feature-dependent judge biases. These estimates are then used to infer the new model's human-reference score from its judge comparisons. The framework allows the feature distribution to change between historical and new-model comparisons. For inference, we construct a Neyman-orthogonal estimator through a joint Riesz correction that removes the first-order effects of estimating the historical human scores, judge sensitivities, and bias functions. We establish identification, convergence rates, and asymptotic normality with consistently estimable variance, and show that ANCHOR attains the semiparametric efficiency bound. Simulations demonstrate gains in score estimation and ranking accuracy. On Chatbot Arena, ANCHOR achieves the lowest score RMSE and insertion MAE among competing methods, with narrower score intervals on average.

math.ST↗