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The design of Proportional-Integral Observer (PIO) for time-delay systems is considered in this paper. First, a method for PIO design is proposed to attenuate the disturbance to a pre-specified level while estimating the state of the delay system. The method guarantees the stability of the observer and minimizes the Hinfin norm between the disturbance and the estimated error. An alternative design...
Estimation fusion for multisensor asynchronous sampling system is currently a hot subject of research. Under the assumption with independent and uncorrelated noises, some asynchronous fusion algorithms have been provided. Unfortunately, the general optimal solution with real-time update performance in linear minimum mean square error (LMMSE) hasn't been given up to now. Simultaneously, it is difficult...
Uncertainty almost exists in the measurements of sensors because of the influence of environment and communication. The uncertainties can be reflected in the loss of measurement data and in the unknown disturbance added on the sensor measurements. In this paper, a linear unbiased minimum variance state filter is designed for discrete-time linear stochastic systems with data loss and unknown disturbance,...
This paper studies the state estimation problem for linear discrete-time systems based on the minimum mean square error (MMSE) criterion. Under the Gaussian assumption on the predicted density, the quantized MMSE filter is derived which has a similar form as the Kalman filter with the raw measurement simply replaced by its quantized version. The quantization effects are explicitly quantified by adding...
The problem of joint input and state estimation is addressed in this paper for linear discrete-time stochastic systems without direct feedthrough from unknown inputs to outputs. With the weighted least squares estimation for an extended state vector including unknown inputs and states, a recursive filter approach referred to as Kalman filter with unknown inputs without direct feedthrough (KF-UI-WDF)...
In this work, we propose a framework for supervisory cooperative estimation of multi-agent nonlinear systems. We introduce a group of sub-observers, each estimating certain states conditioned on certain given input, output, and state information. The cooperation among the sub-observers is supervised by a discrete-event system (DES). The supervisor makes decisions on selecting and configuring a set...
In this paper we consider the state estimation carried over a sensor network. At each time step, only a subset of all sensors are selected to send their observations to the fusion center, where a Kalman filter is implemented to perform the state estimation. The sensors are selected to maximize the lifetime of the network while maintaining a desired quality of state estimation accuracy. We propose...
We consider the problem of state estimation of a discrete time process over an unreliable communication network. Most previous work in literature focus on either quantization effect or packet drops or delays, with a few work considering those uncertainties simultaneously. In this paper, we first present two quantized filter design methods based on a logarithmic quantizer. The first method characterizes...
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