35,454 research outputs found
Joint Estimation and Localization in Sensor Networks
This paper addresses the problem of collaborative tracking of dynamic targets
in wireless sensor networks. A novel distributed linear estimator, which is a
version of a distributed Kalman filter, is derived. We prove that the filter is
mean square consistent in the case of static target estimation. When large
sensor networks are deployed, it is common that the sensors do not have good
knowledge of their locations, which affects the target estimation procedure.
Unlike most existing approaches for target tracking, we investigate the
performance of our filter when the sensor poses need to be estimated by an
auxiliary localization procedure. The sensors are localized via a distributed
Jacobi algorithm from noisy relative measurements. We prove strong convergence
guarantees for the localization method and in turn for the joint localization
and target estimation approach. The performance of our algorithms is
demonstrated in simulation on environmental monitoring and target tracking
tasks.Comment: 9 pages (two-column); 5 figures; Manuscript submitted to the 2014
IEEE Conference on Decision and Control (CDC
Rigid Body Motion Estimation based on the Lagrange-d'Alembert Principle
Stable estimation of rigid body pose and velocities from noisy measurements,
without any knowledge of the dynamics model, is treated using the
Lagrange-d'Alembert principle from variational mechanics. With body-fixed
optical and inertial sensor measurements, a Lagrangian is obtained as the
difference between a kinetic energy-like term that is quadratic in velocity
estimation error and the sum of two artificial potential functions; one
obtained from a generalization of Wahba's function for attitude estimation and
another which is quadratic in the position estimate error. An additional
dissipation term that is linear in the velocity estimation error is introduced,
and the Lagrange-d'Alembert principle is applied to the Lagrangian with this
dissipation. This estimation scheme is discretized using discrete variational
mechanics. The presented pose estimator requires optical measurements of at
least three inertially fixed landmarks or beacons in order to estimate
instantaneous pose. The discrete estimation scheme can also estimate velocities
from such optical measurements. In the presence of bounded measurement noise in
the vector measurements, numerical simulations show that the estimated states
converge to a bounded neighborhood of the actual states.Comment: My earlier submitted manuscript (arXiv:1508.07671), is an extended
version of this work, containing detailed proofs and more elaborated
numerical simulations, currently under review in Automatica. This paper will
be cited in the extended journal version (arXiv:1508.07671) upon publicatio
Asymptotic Analysis of MAP Estimation via the Replica Method and Applications to Compressed Sensing
The replica method is a non-rigorous but well-known technique from
statistical physics used in the asymptotic analysis of large, random, nonlinear
problems. This paper applies the replica method, under the assumption of
replica symmetry, to study estimators that are maximum a posteriori (MAP) under
a postulated prior distribution. It is shown that with random linear
measurements and Gaussian noise, the replica-symmetric prediction of the
asymptotic behavior of the postulated MAP estimate of an n-dimensional vector
"decouples" as n scalar postulated MAP estimators. The result is based on
applying a hardening argument to the replica analysis of postulated posterior
mean estimators of Tanaka and of Guo and Verdu.
The replica-symmetric postulated MAP analysis can be readily applied to many
estimators used in compressed sensing, including basis pursuit, lasso, linear
estimation with thresholding, and zero norm-regularized estimation. In the case
of lasso estimation the scalar estimator reduces to a soft-thresholding
operator, and for zero norm-regularized estimation it reduces to a
hard-threshold. Among other benefits, the replica method provides a
computationally-tractable method for precisely predicting various performance
metrics including mean-squared error and sparsity pattern recovery probability.Comment: 22 pages; added details on the replica symmetry assumptio
Detection of False Data Injection Attacks in Smart Grid under Colored Gaussian Noise
In this paper, we consider the problems of state estimation and false data
injection detection in smart grid when the measurements are corrupted by
colored Gaussian noise. By modeling the noise with the autoregressive process,
we estimate the state of the power transmission networks and develop a
generalized likelihood ratio test (GLRT) detector for the detection of false
data injection attacks. We show that the conventional approach with the
assumption of Gaussian noise is a special case of the proposed method, and thus
the new approach has more applicability. {The proposed detector is also tested
on an independent component analysis (ICA) based unobservable false data attack
scheme that utilizes similar assumptions of sample observation.} We evaluate
the performance of the proposed state estimator and attack detector on the IEEE
30-bus power system with comparison to conventional Gaussian noise based
detector. The superior performance of {both observable and unobservable false
data attacks} demonstrates the effectiveness of the proposed approach and
indicates a wide application on the power signal processing.Comment: 8 pages, 4 figures in IEEE Conference on Communications and Network
Security (CNS) 201
On the applicability of integrated circuit technology to general aviation orientation estimation
The criteria of the significant value of the panel instruments used in general aviation were examined and kinematic equations were added for comparison. An instrument survey was performed to establish the present state of the art in linear and angular accelerometers, pressure transducers, and magnetometers. A very preliminary evaluation was done of the computers available for data evaluation and estimator mechanization. The mathematical model of a light twin aircraft employed in the evaluation was documented, the results of the sensor survey and the results of the design studies were presented
Statistical Modeling and Estimation of Censored Pathloss Data
Pathloss is typically modeled using a log-distance power law with a
large-scale fading term that is log-normal. However, the received signal is
affected by the dynamic range and noise floor of the measurement system used to
sound the channel, which can cause measurement samples to be truncated or
censored. If the information about the censored samples are not included in the
estimation method, as in ordinary least squares estimation, it can result in
biased estimation of both the pathloss exponent and the large scale fading.
This can be solved by applying a Tobit maximum-likelihood estimator, which
provides consistent estimates for the pathloss parameters. This letter provides
information about the Tobit maximum-likelihood estimator and its asymptotic
variance under certain conditions.Comment: 4 pages, 3 figures. Published in IEEE Wireless Communication Letter
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