This paper considers H∞ filtering for rectangular descriptor systems with unknown inputs that affect both the system and the output. An optimal H∞ filter is developed based on the maximum likelihood descriptor Kalman filtering (DKF) method. The developed H∞ filter serves as a unified solution to solve H∞ and Kalman filtering for descriptor systems and standard systems with or without unknown in...

The original Moog ladder filter remains a highly desirable building block for electronic music, both in its original form and in the form of digital simulations. There is now a considerable literature on characterizing the original filter and on techniques for digital simulation. In spite of advances, it is safe to say that there is not yet a definitive method for simulating the Moog ladder fil...

Frequency-domain methods for estimating mixing filters in convolutive blind source separation (BSS) suffer from permutation and scaling indeterminacies in sub-bands. Solving these indeterminacies are critical to such BSS systems. In this paper, we propose to use sparse filter models to tackle the permutation problem. It will be shown that the l1-norm of the filter matrix increases with permutat...

A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF) provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the ...

In some estimation or identification techniques, a forgetting factor has been used to improve the tracking performance for time-varying systems. However, the value of has been typically determined empirically, without any evidence of optimality. In our previous work, this open problem is solved using the framework of H optimization. The resultant H filter enables the forgetting factor to be opt...

One of the most important tasks in integration of GPS/INS is to choose the realistic dynamic model covariance matrix Q and measurement noise covariance matrix R for use in the Kalman filter technique. The performance of the methods to estimate both of these matrices depends entirely on the minimization of dynamic and measurement update errors that lead the filter to converge. This paper evaluat...

Based on the concept of losslessness in digital filter structures, this paper derives a general class of maximally decimated Mchannel quadrature mirror filter hanks that lead to perfect reconstrnction. The perfect-reconstruction property guarantees that the reconstructed signal f ( a ) is a delayed version of the input signal x (n), i.e., 2 ( n ) = n ( n a,,). It is shown that such a property c...

This paper addresses the problem of designing an H∞ filter for a class of nonlinear singularly perturbed systems described by a Takagi-Sugeno (TS) fuzzy model. Based on a linear matrix inequality (LMI) approach, we develop a fuzzy H∞ filter that guarantees the L2-gain from an exogenous input to a filter error to be less than or equal to a prescribed value. In order to alleviate the ill-conditio...

This is a survey paper.The performance of the Kalman filter (KF), which is Algoritstandard as an outstanding implementation for dynamic system state estimation, greatly depends on its parameter R, called the measurement noise covariance matrix. . However, it’s difficult to obtain the accurate value of R before the filter starts, and the value of R is possible to change with the measurement envi...

We address the problem of inferring an undirected graph from nodal observations, which are modeled as nonstationary graph signals generated by local diffusion dynamics that depend on the structure of the unknown network. Using the so-called graph-shift operator (GSO), which is a matrix representation of the graph, we first identify the eigenvectors of the shift matrix from realizations of the d...