15,706 research outputs found
Simultaneous Mode, Input and State Estimation for Switched Linear Stochastic Systems
In this paper, we propose a filtering algorithm for simultaneously estimating
the mode, input and state of hidden mode switched linear stochastic systems
with unknown inputs. Using a multiple-model approach with a bank of linear
input and state filters for each mode, our algorithm relies on the ability to
find the most probable model as a mode estimate, which we show is possible with
input and state filters by identifying a key property, that a particular
residual signal we call generalized innovation is a Gaussian white noise. We
also provide an asymptotic analysis for the proposed algorithm and provide
sufficient conditions for asymptotically achieving convergence to the true
model (consistency), or to the 'closest' model according to an
information-theoretic measure (convergence). A simulation example of
intention-aware vehicles at an intersection is given to demonstrate the
effectiveness of our approach.Comment: Submitted to SIAM Journal on Control and Optimizatio
Nonlinear Unknown Input and State Estimation Algorithm in Mobile Robots
This technical report provides the description and the derivation of a novel
nonlinear unknown input and state estimation algorithm (NUISE) for mobile
robots. The algorithm is designed for real-world robots with nonlinear dynamic
models and subject to stochastic noises on sensing and actuation. Leveraging
sensor readings and planned control commands, the algorithm detects and
quantifies anomalies on both sensors and actuators. Later, we elaborate the
dynamic models of two distinctive mobile robots for the purpose of
demonstrating the application of NUISE. This report serves as a supplementary
document for [1].Comment: arXiv admin note: text overlap with arXiv:1708.0183
Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective
In this paper, we obtain generic bounds on the variances of estimation and
prediction errors in time series analysis via an information-theoretic
approach. It is seen in general that the error bounds are determined by the
conditional entropy of the data point to be estimated or predicted given the
side information or past observations. Additionally, we discover that in order
to achieve the prediction error bounds asymptotically, the necessary and
sufficient condition is that the "innovation" is asymptotically white Gaussian.
When restricted to Gaussian processes and 1-step prediction, our bounds are
shown to reduce to the Kolmogorov-Szeg\"o formula and Wiener-Masani formula
known from linear prediction theory
Switching and Data Injection Attacks on Stochastic Cyber-Physical Systems: Modeling, Resilient Estimation and Attack Mitigation
In this paper, we consider the problem of attack-resilient state estimation,
that is to reliably estimate the true system states despite two classes of
attacks: (i) attacks on the switching mechanisms and (ii) false data injection
attacks on actuator and sensor signals, in the presence of unbounded stochastic
process and measurement noise signals. We model the systems under attack as
hidden mode stochastic switched linear systems with unknown inputs and propose
the use of a multiple-model inference algorithm to tackle these security
issues. Moreover, we characterize fundamental limitations to resilient
estimation (e.g., upper bound on the number of tolerable signal attacks) and
discuss the topics of attack detection, identification and mitigation under
this framework. Simulation examples of switching and false data injection
attacks on a benchmark system and an IEEE 68-bus test system show the efficacy
of our approach to recover resilient (i.e., asymptotically unbiased) state
estimates as well as to identify and mitigate the attacks
Second-Order Fault Tolerant Extended Kalman Filter for Discrete Time Nonlinear Systems
As missing sensor data may severely degrade the overall system performance and stability, reliable state estimation is of great importance in modern data-intensive control, computing, and power systems applications. Aiming at providing a more robust and resilient state estimation technique, this paper presents a novel second-order fault-tolerant extended Kalman filter estimation framework for discrete-time stochastic nonlinear systems under sensor failures, bounded observer-gain perturbation, extraneous noise, and external disturbances condition. The failure mechanism of multiple sensors is assumed to be independent of each other with various malfunction rates. The proposed approach is a locally unbiased, minimum estimation error covariance based nonlinear observer designed for dynamic state estimation under these conditions. It has been successfully applied to a benchmark target-trajectory tracking application. Computer simulation studies have demonstrated that the proposed second-order fault-tolerant extended Kalman filter provides more accurate estimation results, in comparison with traditional first- and second-order extended Kalman filter. Experimental results have demonstrated that the proposed second-order fault-tolerant extended Kalman filter can serve as a powerful alternative to the existing nonlinear estimation approaches
Nonlinear Bayesian Estimation: From Kalman Filtering to a Broader Horizon
This article presents an up-to-date tutorial review of nonlinear Bayesian
estimation. State estimation for nonlinear systems has been a challenge
encountered in a wide range of engineering fields, attracting decades of
research effort. To date, one of the most promising and popular approaches is
to view and address the problem from a Bayesian probabilistic perspective,
which enables estimation of the unknown state variables by tracking their
probabilistic distribution or statistics (e.g., mean and covariance)
conditioned on the system's measurement data. This article offers a systematic
introduction of the Bayesian state estimation framework and reviews various
Kalman filtering (KF) techniques, progressively from the standard KF for linear
systems to extended KF, unscented KF and ensemble KF for nonlinear systems. It
also overviews other prominent or emerging Bayesian estimation methods
including the Gaussian filtering, Gaussian-sum filtering, particle filtering
and moving horizon estimation and extends the discussion of state estimation
forward to more complicated problems such as simultaneous state and
parameter/input estimation
Exploiting Physical Dynamics to Detect Actuator and Sensor Attacks in Mobile Robots
Mobile robots are cyber-physical systems where the cyberspace and the
physical world are strongly coupled. Attacks against mobile robots can
transcend cyber defenses and escalate into disastrous consequences in the
physical world. In this paper, we focus on the detection of active attacks that
are capable of directly influencing robot mission operation. Through leveraging
physical dynamics of mobile robots, we develop RIDS, a novel robot intrusion
detection system that can detect actuator attacks as well as sensor attacks for
nonlinear mobile robots subject to stochastic noises. We implement and evaluate
a RIDS on Khepera mobile robot against concrete attack scenarios via various
attack channels including signal interference, sensor spoofing, logic bomb, and
physical damage. Evaluation of 20 experiments shows that the averages of false
positive rates and false negative rates are both below 1%. Average detection
delay for each attack remains within 0.40s
A Gaussian process latent force model for joint input-state estimation in linear structural systems
The problem of combined state and input estimation of linear structural
systems based on measured responses and a priori knowledge of structural model
is considered. A novel methodology using Gaussian process latent force models
is proposed to tackle the problem in a stochastic setting. Gaussian process
latent force models (GPLFMs) are hybrid models that combine differential
equations representing a physical system with data-driven non-parametric
Gaussian process models. In this work, the unknown input forces acting on a
structure are modelled as Gaussian processes with some chosen covariance
functions which are combined with the mechanistic differential equation
representing the structure to construct a GPLFM. The GPLFM is then conveniently
formulated as an augmented stochastic state-space model with additional states
representing the latent force components, and the joint input and state
inference of the resulting model is implemented using Kalman filter. The
augmented state-space model of GPLFM is shown as a generalization of the class
of input-augmented state-space models, is proven observable, and is robust
compared to conventional augmented formulations in terms of numerical
stability. The hyperparameters governing the covariance functions are estimated
using maximum likelihood optimization based on the observed data, thus
overcoming the need for manual tuning of the hyperparameters by
trial-and-error. To assess the performance of the proposed GPLFM method,
several cases of state and input estimation are demonstrated using numerical
simulations on a 10-dof shear building and a 76-storey ASCE benchmark office
tower. Results obtained indicate the superior performance of the proposed
approach over conventional Kalman filter based approaches.Comment: Submitted to Mechanical Systems and Signal Processin
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Review of Unbiased FIR Filters, Smoothers, and Predictors for Polynomial Signals
Extracting an estimate of a slowly varying signal corrupted by noise is a common task. Examples can be found in industrial, scientific and biomedical instrumentation. Depending on the nature of the application the signal estimate is allowed to be a delayed estimate of the original signal or, in the other extreme, no delay is tolerated. These cases are commonly referred to as filtering, prediction, and smoothing depending on the amount of advance or lag between the input data set and the output data set. In this review paper we provide a comprehensive set of design and analysis tools for designing unbiased FIR filters, predictors, and smoothers for slowly varying signals, i.e. signals that can be modeled by low order polynomials. Explicit expressions of parameters needed in practical implementations are given. Real life examples are provided including cases where the method is extended to signals that are piecewise slowly varying. A critical view on recursive implementations of the algorithms is provided
Estimating outcome probabilities of quantum circuits using quasiprobabilities
We present a method for estimating the probabilities of outcomes of a quantum
circuit using Monte Carlo sampling techniques applied to a quasiprobability
representation. Our estimate converges to the true quantum probability at a
rate determined by the total negativity in the circuit, using a measure of
negativity based on the 1-norm of the quasiprobability. If the negativity grows
at most polynomially in the size of the circuit, our estimator converges
efficiently. These results highlight the role of negativity as a measure of
non-classical resources in quantum computation.Comment: 5 pages + 1 page appendix, 1 figure, comments welcome; v2 published
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