Brief paper: Multi-rate stochastic H∞ filtering for networked multi-sensor fusion

Bias estimation for asynchronous multi-rate multi-sensor fusion with unknown inputs

This article formulates a multi-rate linear minimum mean squared error (LMMSE) state estimation problem, which includes four rates as follows: the state updating rate in the model, the measurement sampling rate, the estimate updating rate and the estimate output rate. This formulation is unique in two ways. First, the rate ratio between state measurement and state estimate is more general (a rational number), instead of just an integer or its reciprocal as considered in the existing literature. Second, state estimates are produced in blocks, which have never been considered before in the multi-rate estimator design. The multi-rate LMMSE estimation problem is solved by examining several distinctive cases for single-rate state estimation, obtained through the lifting technique. Also, sufficient conditions are given for asymptotic stability of the proposed multi-rate LMMSE estimators. An example in tracking a manoeuvering target is given to illustrate the proposed multi-rate state estimators.

Multi-rate optimal state estimation

In this paper, the finite-time filtering problem is investigated for a class of wireless networked multirate systems (NMSs) with fading channels. The system measurements are transmitted through fading channels described by a modified stochastic Rice fading model, and the event-based wireless relay communication scheme is proposed to determine whether the received fading measurements in the relay nodes should be transmitted to the filter. With the aid of the stochastic analysis approach, sufficient conditions are established under which the stochastic finite-time boundedness (FTB) of the estimation error dynamics is guaranteed and the prescribed finite-time $H_{\infty }$ performance constraint is achieved. Based on the derived conditions, the addressed finite-time filtering problem of NMSs is recast as a convex optimization one that can be solved via the semidefinite program method. Furthermore, the explicit expression of the desired filter is obtained by means of the feasibility of certain matrix inequalities. Two additional optimization problems are considered with respect to the FTB parameter and the finite-time $H_{\infty }$ performance index. Finally, a numerical simulation example on a continuous stirred tank reactor is employed to show the effectiveness of the filtering scheme proposed in this paper.

Event-Based Finite-Time Filtering for Multirate Systems With Fading Measurements

Model-reduced fault detection for multi-rate sensor fusion with unknown inputs

This note presents both optimal and suboptimal filtering algorithms for estimating state variables based on measurements sampled at two different data rates. The optimal algorithm consists of two parallel Kalman filters; one processes the fast rate measurement and is of reduced-order, and the other processes the residuals from the first filter along with the slow rate measurement. This algorithm is used to design a suboptimal algorithm that has decreased computational requirements with only a small performance penalty.

Ching-Fu Gau

Papers

Estimation using a multirate filter