Data‐driven modeling from noisy measurements
نویسندگان
چکیده
منابع مشابه
Distributed centroid estimation from noisy relative measurements
We propose an anchorless distributed technique for estimating the centroid of a network of agents from noisy relative measurements. The positions of the agents are then obtained relative to the estimated centroid. The usual approach to multi-agent localization assumes instead that one anchor agent exists in the network, and the other agents positions are estimated with respect to the anchor. We...
متن کاملNon-Convex Compressed Sensing from Noisy Measurements
This paper proposes solution to the following non-convex optimization problem: min || x || p subject to || y Ax || q Such an optimization problem arises in a rapidly advancing branch of signal processing called ‘Compressed Sensing’ (CS). The problem of CS is to reconstruct a k-sparse vector xnX1, from noisy measurements y = Ax+ , where AmXn (m<n) is the measurement matrix and mX1 is additive no...
متن کامل3D Modeling from AFM Measurements
There exist many techniques for the measurement of micro and nano surfaces and also several conventional ways to represent the resulting data, such as pseudo color or isometric 3D. This paper addresses the problem of building complete 3D micro-object models from measurements in the submicrometric range. More specifically, it considers measurements provided by an atomic-force microscope (AFM) an...
متن کاملSharp Support Recovery from Noisy Random Measurements by L1 minimization
In this paper, we investigate the theoretical guarantees of penalized l1-minimization (also called Basis Pursuit Denoising or Lasso) in terms of sparsity pattern recovery (support and sign consistency) from noisy measurements with non-necessarily random noise, when the sensing operator belongs to the Gaussian ensemble (i.e. random design matrix with i.i.d. Gaussian entries). More precisely, we ...
متن کاملError Scaling in Position Estimation from Noisy Relative Pose Measurements
We examine how fast the estimation error grows with time when a mobile robot/vehicle estimates its location from relative pose measurements without global position or orientation sensors. We show that both bias and variance of the position estimation error grows at most linearly with time (or distance traversed) asymptotically. The bias growth rate is crucially dependent on the trajectory of th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PAMM
سال: 2021
ISSN: 1617-7061,1617-7061
DOI: 10.1002/pamm.202000358