184 research outputs found
Optimal Fusion Estimation with Multi-Step Random Delays and Losses in Transmission
This paper is concerned with the optimal fusion estimation problem in networked stochastic systems with bounded random delays and packet dropouts, which unavoidably occur during the data transmission in the network. The measured outputs from each sensor are perturbed by random parameter matrices and white additive noises, which are cross-correlated between the different sensors. Least-squares fusion linear estimators including filter, predictor and fixed-point smoother, as well as the corresponding estimation error covariance matrices are designed via the innovation analysis approach. The proposed recursive algorithms depend on the delay probabilities at each sampling time, but do not to need to know if a particular measurement is delayed or not. Moreover, the knowledge of the signal evolution model is not required, as the algorithms need only the first and second order moments of the processes involved. Some of the practical situations covered by the proposed system model with random parameter matrices are analyzed and the influence of the delays in the estimation accuracy are examined in a numerical example.This research is supported by the âMinisterio de EconomĂa y Competitividadâ and âFondo
Europeo de Desarrollo Regionalâ FEDER (Grant No. MTM2014-52291-P)
Active Classification for POMDPs: a Kalman-like State Estimator
The problem of state tracking with active observation control is considered
for a system modeled by a discrete-time, finite-state Markov chain observed
through conditionally Gaussian measurement vectors. The measurement model
statistics are shaped by the underlying state and an exogenous control input,
which influence the observations' quality. Exploiting an innovations approach,
an approximate minimum mean-squared error (MMSE) filter is derived to estimate
the Markov chain system state. To optimize the control strategy, the associated
mean-squared error is used as an optimization criterion in a partially
observable Markov decision process formulation. A stochastic dynamic
programming algorithm is proposed to solve for the optimal solution. To enhance
the quality of system state estimates, approximate MMSE smoothing estimators
are also derived. Finally, the performance of the proposed framework is
illustrated on the problem of physical activity detection in wireless body
sensing networks. The power of the proposed framework lies within its ability
to accommodate a broad spectrum of active classification applications including
sensor management for object classification and tracking, estimation of sparse
signals and radar scheduling.Comment: 38 pages, 6 figure
Networked fusion estimation with multiple uncertainties and time-correlated channel noise
This paper is concerned with the fusion filtering and fixed-point smoothing problems for a class of networked
systems with multiple random uncertainties in both the sensor outputs and the transmission connections. To deal
with this kind of systems, random parameter matrices are considered in the mathematical models of both the
sensor measurements and the data available after transmission. The additive noise in the transmission channel
from each sensor is assumed to be sequentially time-correlated. By using the time-differencing approach, the
available measurements are transformed into an equivalent set of observations that do not depend on the timecorrelated
noise. The innovation approach is then applied to obtain recursive distributed and centralized fusion
estimation algorithms for the filtering and fixed-point smoothing estimators of the signal based on the transformed
measurements, which are equal to the estimators based on the original ones. The derivation of the algorithms
does not require the knowledge of the signal evolution model, but only the mean and covariance functions of
the processes involved (covariance information). A simulation example illustrates the utility and effectiveness of
the proposed fusion estimation algorithms, as well as the applicability of the current model to deal with different
network-induced random phenomena.This research is supported by Ministerio de EconomĂa, Industria y Competitividad, Agencia Estatal de InvestigaciĂłn and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)
Distributed Fusion Estimation with Sensor Gain Degradation and Markovian Delays
This paper investigates the distributed fusion estimation of a signal for a class of multi-sensor
systems with random uncertainties both in the sensor outputs and during the transmission connections.
The measured outputs are assumed to be affected by multiplicative noises, which degrade the signal,
and delays may occur during transmission. These uncertainties are commonly described by means of
independent Bernoulli random variables. In the present paper, the model is generalised in two directions:
(i) at each sensor, the degradation in the measurements is modelled by sequences of random variables
with arbitrary distribution over the interval [0, 1]; (ii) transmission delays are described using three-state
homogeneous Markov chains (Markovian delays), thus modelling dependence at different sampling
times. Assuming that the measurement noises are correlated and cross-correlated at both simultaneous
and consecutive sampling times, and that the evolution of the signal process is unknown, we address the
problem of signal estimation in terms of covariances, using the following distributed fusion method. First,
the local filtering and fixed-point smoothing algorithms are obtained by an innovation approach. Then,
the corresponding distributed fusion estimators are obtained as a matrix-weighted linear combination
of the local ones, using the mean squared error as the criterion of optimality. Finally, the efficiency of
the algorithms obtained, measured by estimation error covariance matrices, is shown by a numerical
simulation example.Ministerio de EconomĂa, Industria y CompetitividadEuropean Union (EU)
MTM2017-84199-PAgencia Estatal de InvestigaciĂł
On-Manifold Preintegration for Real-Time Visual-Inertial Odometry
Current approaches for visual-inertial odometry (VIO) are able to attain
highly accurate state estimation via nonlinear optimization. However, real-time
optimization quickly becomes infeasible as the trajectory grows over time, this
problem is further emphasized by the fact that inertial measurements come at
high rate, hence leading to fast growth of the number of variables in the
optimization. In this paper, we address this issue by preintegrating inertial
measurements between selected keyframes into single relative motion
constraints. Our first contribution is a \emph{preintegration theory} that
properly addresses the manifold structure of the rotation group. We formally
discuss the generative measurement model as well as the nature of the rotation
noise and derive the expression for the \emph{maximum a posteriori} state
estimator. Our theoretical development enables the computation of all necessary
Jacobians for the optimization and a-posteriori bias correction in analytic
form. The second contribution is to show that the preintegrated IMU model can
be seamlessly integrated into a visual-inertial pipeline under the unifying
framework of factor graphs. This enables the application of
incremental-smoothing algorithms and the use of a \emph{structureless} model
for visual measurements, which avoids optimizing over the 3D points, further
accelerating the computation. We perform an extensive evaluation of our
monocular \VIO pipeline on real and simulated datasets. The results confirm
that our modelling effort leads to accurate state estimation in real-time,
outperforming state-of-the-art approaches.Comment: 20 pages, 24 figures, accepted for publication in IEEE Transactions
on Robotics (TRO) 201
Intelligent Sensor Positioning and Orientation Through Constructive Neural Network-Embedded INS/GPS Integration Algorithms
Mobile mapping systems have been widely applied for acquiring spatial information in applications such as spatial information systems and 3D city models. Nowadays the most common technologies used for positioning and orientation of a mobile mapping system include a Global Positioning System (GPS) as the major positioning sensor and an Inertial Navigation System (INS) as the major orientation sensor. In the classical approach, the limitations of the Kalman Filter (KF) method and the overall price of multi-sensor systems have limited the popularization of most land-based mobile mapping applications. Although intelligent sensor positioning and orientation schemes consisting of Multi-layer Feed-forward Neural Networks (MFNNs), one of the most famous Artificial Neural Networks (ANNs), and KF/smoothers, have been proposed in order to enhance the performance of low cost Micro Electro Mechanical System (MEMS) INS/GPS integrated systems, the automation of the MFNN applied has not proven as easy as initially expected. Therefore, this study not only addresses the problems of insufficient automation in the conventional methodology that has been applied in MFNN-KF/smoother algorithms for INS/GPS integrated systems proposed in previous studies, but also exploits and analyzes the idea of developing alternative intelligent sensor positioning and orientation schemes that integrate various sensors in more automatic ways. The proposed schemes are implemented using one of the most famous constructive neural networksâthe Cascade Correlation Neural Network (CCNNs)âto overcome the limitations of conventional techniques based on KF/smoother algorithms as well as previously developed MFNN-smoother schemes. The CCNNs applied also have the advantage of a more flexible topology compared to MFNNs. Based on the experimental data utilized the preliminary results presented in this article illustrate the effectiveness of the proposed schemes compared to smoother algorithms as well as the MFNN-smoother schemes
Signal Estimation with Random Parameter Matrices and Time-correlated Measurement Noises
This paper is concerned with the least-squares linear estimation problem for a class of discrete-time networked
systems whose measurements are perturbed by random parameter matrices and time-correlated additive noise,
without requiring a full knowledge of the state-space model generating the signal process, but only information
about its mean and covariance functions. Assuming that the measurement additive noise is the output of a
known linear systemdriven by white noise, the time-differencing method is used to remove this time-correlated
noise and recursive algorithms for the linear filtering and fixed-point smoothing estimators are obtained by an
innovation approach. These estimators are optimal in the least-squares sense and, consequently, their accuracy
is evaluated by the estimation error covariance matrices, for which recursive formulas are also deduced. The
proposed algorithms are easily implementable, as it is shown in the computer simulation example, where they
are applied to estimate a signal from measured outputs which, besides including time-correlated additive noise,
are affected by the missing measurement phenomenon and multiplicative noise (random uncertainties that can
be covered by the current model with random parameter matrices). The computer simulations also illustrate
the behaviour of the filtering estimators for different values of the missing measurement probability.Ministerio de EconomĂa, Industria y CompetitividadAgencia Estatal de InvestigaciĂłnEuropean Union (EU)
MTM201784199-
State Estimation and Smoothing for the Probability Hypothesis Density Filter
Tracking multiple objects is a challenging problem for an automated system,
with applications in many domains. Typically the system must be able to
represent the posterior distribution of the state of the targets, using a recursive
algorithm that takes information from noisy measurements. However, in
many important cases the number of targets is also unknown, and has also
to be estimated from data.
The Probability Hypothesis Density (PHD) filter is an effective approach
for this problem. The method uses a first-order moment approximation to
develop a recursive algorithm for the optimal Bayesian filter. The PHD
recursion can implemented in closed form in some restricted cases, and more
generally using Sequential Monte Carlo (SMC) methods. The assumptions
made in the PHD filter are appealing for computational reasons in real-time
tracking implementations. These are only justifiable when the signal to noise
ratio (SNR) of a single target is high enough that remediates the loss of
information from the approximation.
Although the original derivation of the PHD filter is based on functional
expansions of belief-mass functions, it can also be developed by exploiting elementary
constructions of Poisson processes. This thesis presents novel strategies
for improving the Sequential Monte Carlo implementation of PHD filter
using the point process approach. Firstly, we propose a post-processing state
estimation step for the PHD filter, using Markov Chain Monte Carlo methods
for mixture models. Secondly, we develop recursive Bayesian smoothing
algorithms using the approximations of the filter backwards in time. The
purpose of both strategies is to overcome the problems arising from the PHD
filter assumptions. As a motivating example, we analyze the performance of
the methods for the difficult problem of person tracking in crowded environment
- âŠ