Structure and realization of pole-shared switched-current complex wavelet filter

A pole-shared switched-current complex wavelet filter structure with follow-the-leader feedback configuration is proposed for synthesizing the real and imaginary approximation functions with the same poles. The double-sampling fully-balanced SI bilinear integrator and current mirror are employed as the building cells. By sharing the implementation circuit for approximation poles of the real and the imaginary part, the proposed structure only has the same circuit complexity as that of real-valued wavelet filter, which is very suitable for small-size and low-power application. The complex Morlet wavelet is selected as an example to elaborate the design procedure. Simulation results confirm that the presented complex wavelet filter structure can generate the real and imaginary coefficients of complex wavelet transform accurately with simple synthesis method and explicit design formulas.Peer reviewedFinal Accepted Versio

On The Use Of Gaussian Filter Functions For Adaptive Optics

For adaptive optic systems, the use of aperture filter functions calculated using various Zernike modes can be useful in removing lower-order aberrations caused by atmospheric turbulence. Traditionally, these filter functions are calculated using the step function depicting a hard aperture that introduces integrals that are sometimes difficult to integrate and must be done numerically. The Gaussian method can be used in place of the conventional method for calculating the aperture filter functions. Evaluation of the Gaussian approximation for modeling a finite receiver aperture can be made by comparison of reduction in phase variance with results achieved using the conventional method. The validity of Gaussian approximation in this application is demonstrated by the consistency of results between the two methodologies. Comparison of reduction in scintillation by the two methodologies reveals several benefits derived from utilization of Gaussian approximation. The Gaussian approximation produces data that can be interpreted analytically. It further produces greater scintillation reduction. This paper will first examine the use of statistical models for predicting atmospheric turbulence and then the use of Zernike polynomials in adaptive optics. Next, this paper compares the reduction of phase variance and scintillation using the conventional method with the Gaussian approximation to evaluate the effectiveness of the new filter functions. The results of these comparisons are presented both as mathematical expressions and graphically

Likelihood Consensus and Its Application to Distributed Particle Filtering

We consider distributed state estimation in a wireless sensor network without a fusion center. Each sensor performs a global estimation task---based on the past and current measurements of all sensors---using only local processing and local communications with its neighbors. In this estimation task, the joint (all-sensors) likelihood function (JLF) plays a central role as it epitomizes the measurements of all sensors. We propose a distributed method for computing, at each sensor, an approximation of the JLF by means of consensus algorithms. This "likelihood consensus" method is applicable if the local likelihood functions of the various sensors (viewed as conditional probability density functions of the local measurements) belong to the exponential family of distributions. We then use the likelihood consensus method to implement a distributed particle filter and a distributed Gaussian particle filter. Each sensor runs a local particle filter, or a local Gaussian particle filter, that computes a global state estimate. The weight update in each local (Gaussian) particle filter employs the JLF, which is obtained through the likelihood consensus scheme. For the distributed Gaussian particle filter, the number of particles can be significantly reduced by means of an additional consensus scheme. Simulation results are presented to assess the performance of the proposed distributed particle filters for a multiple target tracking problem

Constrained L2L^2-approximation by polynomials on subsets of the circle

We study best approximation to a given function, in the least square sense on a subset of the unit circle, by polynomials of given degree which are pointwise bounded on the complementary subset. We show that the solution to this problem, as the degree goes large, converges to the solution of a bounded extremal problem for analytic functions which is instrumental in system identification. We provide a numerical example on real data from a hyperfrequency filter

New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems

This paper describes new approaches to improve the local and global approximation (matching) and modeling capability of Takagi–Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the T-S method because this type of membership function has been widely used during the last 2 decades in the stability, controller design of fuzzy systems and is popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S identification method with optimized performance in approximating nonlinear functions. We propose a noniterative method through weighting of parameters approach and an iterative algorithm by applying the extended Kalman filter, based on the same idea of parameters’ weighting. We show that the Kalman filter is an effective tool in the identification of T-S fuzzy model. A fuzzy controller based linear quadratic regulator is proposed in order to show the effectiveness of the estimation method developed here in control applications. An illustrative example of an inverted pendulum is chosen to evaluate the robustness and remarkable performance of the proposed method locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity, and generality of the algorithm. An illustrative example is chosen to evaluate the robustness. In this paper, we prove that these algorithms converge very fast, thereby making them very practical to use