Journal ArticleDOI
Recursive estimators of signals from measurements with stochastic delays using covariance information
TLDR
Recursive estimation algorithms are obtained without requiring the state-space model generating the signal, but just using covariance information about the signal and the additive noise in the observations as well as the delay probabilities.About:
This article is published in Applied Mathematics and Computation.The article was published on 2005-03-04. It has received 67 citations till now. The article focuses on the topics: White noise & Noise (signal processing).read more
Citations
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Journal ArticleDOI
Brief paper: Optimal linear estimation for systems with multiple packet dropouts
TL;DR: The optimal linear estimators including filter, predictor and smoother are developed via an innovation analysis approach based on a packet dropout model and computed recursively in terms of a Riccati difference equation of dimension equal to the order of the system state plus that of the measurement output.
Journal ArticleDOI
Multi-sensor distributed fusion estimation with applications in networked systems
TL;DR: The advances of DFE algorithms for networked systems are reviewed, including data quantization, random transmission delays, packet dropouts, fading measurements and communication disturbances, and some random phenomena induced by networks are discussed.
Journal ArticleDOI
Gain-Constrained Recursive Filtering With Stochastic Nonlinearities and Probabilistic Sensor Delays
TL;DR: The purpose of the addressed gain-constrained filtering problem is to design a filter such that, for all probabilistic sensor delays, stochastic nonlinearities, gain constraint as well as correlated noises, the cost function concerning the filtering error is minimized at each sampling instant.
Journal ArticleDOI
Optimal Linear Filters for Discrete-Time Systems With Randomly Delayed and Lost Measurements With/Without Time Stamps
TL;DR: A novel model is developed to describe possible random delays and losses of measurements transmitted from a sensor to a filter by a group of Bernoulli distributed random variables and the optimal filter is given by Kalman filter when packets are time-stamped.
Journal ArticleDOI
Optimal Filtering for Systems With Multiple Packet Dropouts
TL;DR: An unbiased optimal filter is developed in the linear least-mean-square sense, whose solution depends on the recursion of a Riccati equation and a Lyapunov equation.
References
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Journal ArticleDOI
Stochastic analysis and control of real-time systems with random time delays
TL;DR: In this article, a new scheme based on stochastic control theory and a separation property is shown to hold for the optimal controller for real-time systems subject to random time delays in the communication network is presented.
Journal ArticleDOI
Stochastic Analysis and Control of Real-Time Systems with Random Time Delays
TL;DR: A new scheme for handling the random time delays is developed and successfully compared with previous schemes, based on stochastic control theory and a separation property is shown to hold for the optimal controller.
Journal ArticleDOI
The problem of state estimation via asynchronous communication channels with irregular transmission times
TL;DR: It is shown that the proposed state estimator is exponentially stable under natural assumptions and the minimum variance state estimation problem is solved.
Journal ArticleDOI
Interconnected Network State Estimation Using Randomly Delayed Measurements
Chun-Lien Su,Chan-Nan Lu +1 more
TL;DR: An implementation of a stochastic extended Kalman filter (EKF) algorithm, which provides optimal estimates of interconnected network states for systems in which some or all measurements are delayed, is presented.
Journal ArticleDOI
State Estimation Using Randomly Delayed Measurements
TL;DR: In this paper, a modification of the conventional minimum variance state estimator is presented to accommodate the effects of randomly varying delays in arrival of sensor data at the controller terminal, where the currently available sensor data is used at each sampling instant to obtain the state estimate which can be used to generate the control signal.