Mean-square H∞ filtering for stochastic systems: Application to a 2DOF helicopter

This paper designs the central finite-dimensional H_∞ filter for linear stochastic systems with integral-quadratically bounded deterministic disturbances, that is suboptimal for a given threshold γ with respect to a modified Bolza-Meyer quadratic criterion including the attenuation control term with the opposite sign. The original H_∞ filtering problem for a linear stochastic system is reduced to the corresponding mean-square H₂ filtering problem, using the technique proposed in Doyle (1989) [1]. In the example, the designed filter is applied to estimation of the pitch and yaw angles of a two degrees of freedom (2DOF) helicopter.     

Optimal nonlinear filtering with constraints

The impulse response of nonlinear systems with limitation of band has been considered. On the base of the minimum mean square error criterion the generalisation of the Wiener-Hopf's equation has been calculated.     

Optimal nonlinear filtering with constraints—Part II

The impulse response of nonlinear systems with bounded memory and energy have been considered. On the base of the minimum mean square error criterion a generalization of the Wiener-Hopf equation is developed.     

Robust state estimation for two-dimensional stochastic time-delay systems with missing measurements and sensor saturation

In this paper, the robust state estimation problem is investigated for a class of uncertain two-dimensional (2-D) systems with state delays and stochastic disturbances. The imperfect measurement output is subject to probabilistic data missing and sensor saturations. The missing phenomenon of the sensor measurement is governed by a stochastic variable satisfying the Bernoulli random binary distribution law, and the sensor saturation is considered to reflect the limited capacity of the communication networks. The parameter uncertainties are assumed to be norm-bounded and enter into the linear part of the system model in both directions. Through available but imperfect output measurements, a state estimator is designed to estimate the system states in the presence of data missing, sensor saturation, parameter uncertainties as well as stochastic perturbations. By defining an energy-like functional and conducting some stochastic analysis, several sufficient criteria in terms of matrix inequalities are established, which not only ensure the existence of the desired estimator gains but also guarantee the globally robustly asymptotic stability in the mean square of the estimation error dynamics. Finally, two numerical examples are exploited to show the effectiveness of the design method proposed in this paper.     

Robust filtering for a class of stochastic uncertain nonlineartime-delay systems via exponential state estimation

We investigate the robust filter design problem for a class of nonlinear time-delay stochastic systems. The system under study involves stochastics, unknown state time-delay, parameter uncertainties, and unknown nonlinear disturbances, which are all often encountered in practice and the sources of instability. The aim of this problem is to design a linear, delayless, uncertainty-independent state estimator such that for all admissible uncertainties as well as nonlinear disturbances, the dynamics of the estimation error is stochastically exponentially stable in the mean square, independent of the time delay. Sufficient conditions are proposed to guarantee the existence of desired robust exponential filters, which are derived in terms of the solutions to algebraic Riccati inequalities. The developed theory is illustrated by numerical simulation     

Joint parameter estimation and symbol detection for linear ornonlinear unknown channels

We present an iterative method for joint channel parameter estimation and symbol selection via the Baum-Welch algorithm, or equivalently the Expectation-Maximization (EM) algorithm. Channel parameters, including noise variance, are estimated using a maximum likelihood criterion. The Markovian properties of the channel state sequence enable us to calculate the required likelihood using a forward-backward algorithm. The calculated likelihood functions can easily give optimum decisions on information symbols which minimize the symbol error probability. The proposed receiver can be used for both linear and nonlinear channels. It improves the system throughput by making saving in the transmission of known symbols, usually employed for channel identification. Simulation results which show fast convergence are presented     

Joint iterative interference cancellation and parameter estimation for cdma systems

This letter proposes a unified approach to joint iterative parameter estimation and interference cancellation (IC) for uplink CDMA systems in multipath channels. A unified framework is presented in which the IC problem is formulated as an optimization problem of an IC parameter vector for each stage and user. We also propose detectors based on a least-squares (LS) joint optimization method for estimating the linear receiver filter front-end, the IC, and the channel parameters. Simulations for the uplink of a synchronous DS-CDMA system show that the proposed methods significantly outperform the best known IC schemes.     

时滞时变对象参数辨识方法

针对时滞时变系统,提出了一种参数辨识的新方法.该算法一方面采用互相关函数来辨识滞后时间,并引进了快速傅里叶变换及其反变换,提高了计算效率;另一方面,在变参数增量估计递推最小二乘算法估计时变参数的基础上,引入误差级序列,改善了时变参数的辨识精度.最后通过仿真验证了该方法的良好性能.     

Biased bearings-only parameter estimation for bistatic system

According to the biased angles provided by the bistatic sensors,the necessary condition of observability and Cramer-Rao low bounds for the bistatic system are derived and analyzed,respectively. Additionally,a dual Kalman filter method is presented with the purpose of eliminating the effect of biased angles on the state variable estimation. Finally,Monte-Carlo simulations are conducted in the observable scenario. Simulation results show that the proposed theory holds true,and the dual Kalman filter method can estimate state variable and biased angles simultaneously. Furthermore,the estimated results can achieve their Cramer-Rao low bounds.     

Closed-loop-based observer approach for tunneling current parameter estimation in an experimental STM

This paper is about parameter estimation in quantum effect of tunneling current, and is based on an experimental device of Scanning Tunneling Microscope (STM) type. Since this effect needs feedback control in order to be obtained and kept, a closed-loop stability analysis is first presented prior to any estimation. Then, in this context of closed-loop operation, an observer approach is proposed to estimate the couple of parameters which characterize the tunneling current (nonlinear) model. An extension of this observer technique to topography estimation is also discussed, and all the methodologies are illustrated with experimental data.     

Joint state and parameter estimation for a target-directed nonlinear dynamic system model

We present a new approach to joint state and parameter estimation for a target-directed, nonlinear dynamic system model with switching states. The model, recently proposed for representing speech dynamics, is called the hidden dynamic model (HDM). The model parameters, subject to statistical estimation, consist of the target vector and the system matrix (also called "time-constants"), as well as parameters characterizing the nonlinear mapping from the hidden state to the observation. We implement these parameters as the weights of a three-layer feedforward multilayer perceptron (MLP) network. The new estimation approach is based on the extended Kalman filter (EKF), and its performance is compared with the traditional expectation-maximization (EM) based approach. Extensive simulation results are presented using both approaches and under typical HDM speech modeling conditions. The EKF-based algorithm demonstrates superior convergence performance compared with the EM algorithm, but the former suffers from excessive computational loads when adopted for training the MLP weights. In all cases, the simulated model output converges to the given observation sequence. However, only in the case where the MLP weights or the target vector are assumed known do the time-constant parameters converge to their true values. We also show that the MLP weights never converge to their true values, thus demonstrating the many-to-one mapping property of the feedforward MLP. We conclude that, for the system to be identifiable, restrictions on the parameter space are needed.     

The data filtering based generalized stochastic gradient parameter estimation algorithms for multivariate output-error autoregressive systems using the auxiliary model

Multidimensional Systems and Signal Processing - Parameter estimation has wide applications in one-dimensional and multidimensional signal processing and filtering. This paper focuses on the...     

GMSK混合信号时延的并行估计算法

在GMSK信号线性近似的基础上,提出一种GMSK混合信号时延的并行估计算法.利用过采样混合信号的自相关函数建立平坦衰落条件下GMSK混合信号时延参数的并行估计方程组,并推导了并行时延估计的克拉美劳界.针对估计方程组中GMSK成型滤波器的复杂非线性特性,采用多项式拟合方法进行简化并利用收敛速度较快的梯度下降法进行数值求解.仿真结果表明该算法对GMSK混合信号时延参数的估计较准确,其估计误差方差与时延估计性能的克拉美劳界较为接近.该时延并行估计算法具有不需要前导字、计算量较小的优点,为混合信号的进一步盲处理打下基础,具有实际意义.     

时滞和滤波联合辨识的递推最小二乘算法

时滞和滤波联合辨识问题既是经典时滞估计问题的推广，又是自适应系统建模和时灌估计两方面的交叉。本文针对先滤波后时滞的系统模型，从方法上改进BOUDREAU＆KABAL提出的基于快速横向滤波器的递谁最小二乘滤波算法，使其时间复杂度由O［19ρ］下降为0［7ρ］，便于实时在线应用。我们以低通滤波器与线性时滞串联系统的辨识为例，表现该算法对变化时滞的跟踪能力及联合辨识性能。     

Joint symbol detection and channel parameter estimation inasynchronous DS-CDMA systems

A method for jointly estimating the time delay and complex gain parameters, as well as detecting the transmitted symbols in an asynchronous multipath DS-CDMA system, is presented. A short training sequence is used to obtain a coarse estimate of the channel parameters, which is consequently used to detect the symbols. By exploiting structure in the digitally modulated signals, the method iterates to (i) improve the estimate of the channel parameters and (ii) reduce the probability of incorrect detection. The method's efficacy is demonstrated by numerical simulations     

Robust H/sub /spl infin// filtering for stochastic time-delay systems with missing measurements

In this paper, the robust H/sub /spl infin// filtering problem is studied for stochastic uncertain discrete time-delay systems with missing measurements. The missing measurements are described by a binary switching sequence satisfying a conditional probability distribution. We aim to design filters such that, for all possible missing observations and all admissible parameter uncertainties, the filtering error system is exponentially mean-square stable, and the prescribed H/sub /spl infin// performance constraint is met. In terms of certain linear matrix inequalities (LMIs), sufficient conditions for the solvability of the addressed problem are obtained. When these LMIs are feasible, an explicit expression of a desired robust H/sub /spl infin// filter is also given. An optimization problem is subsequently formulated by optimizing the H/sub /spl infin// filtering performances. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed design approach.     

基于参数估计的降晰函数辨识及图像复原算法

成像系统的点扩展函数(PSF)以及观测噪声,在一般应用过程中是未知信息,因此,点扩展函数的辨识是一个具有挑战性的世界难题.为解决实际工作中遇到的在已知降晰类型情况下的降晰函数辨识和降晰图像复原问题,提出了基于参数估计的降晰函数辨识及降晰图像复原算法.首先,由初始猜测给定降晰函数参数的变化范围和参数的增量步长;然后,最小化降晰图像和由相应点扩展函数及降晰图像得到的实验观测图像的差的Frobenius范数,以确定点扩展函数的参数,进而确定降晰图像的点扩展函数并对降晰图像进行复原.应用基于Wiener滤波的频域循环边界算法对降晰图像进行复原.实验结果表明:在降晰图像信噪比较高的情况下,降晰函数的辨识结果是可靠和准确的,有较好的复原效果.     

Improved parameter estimation by noise compensation in the time-scale domain

It was shown recently that parameter estimation can be performed directly in the time-scale domain by isolating regions wherein the prediction error can be attributed to the error of individual dynamic model parameters [1]. Based on these single-parameter equations of the prediction error, individual model parameters error can be estimated for iterative parameter estimation. An added benefit of this parameter estimation method, besides its unique convergence characteristics, is the added capacity for direct noise compensation in the time-scale domain. This paper explores this benefit by introducing a noise compensation method that estimates the distortion by noise of the prediction error in the time-scale domain and incorporates that as a confidence factor to bias the estimation of individual parameters error. This method is shown to improve the precision of the estimated parameters when the confidence factors accurately represent the noise distortion of the prediction error.     

Recursive state estimation for linear dynamic systems under algebraic constraints

A minimum variance recursive linear estimator is discussed for linear dynamic systems where the state vector is also subject to linear algebraic equality constraints.<>     

Particle filters for state estimation of jump Markov linear systems

Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time according to a finite state Markov chain. In this paper, our aim is to recursively compute optimal state estimates for this class of systems. We present efficient simulation-based algorithms called particle filters to solve the optimal filtering problem as well as the optimal fixed-lag smoothing problem. Our algorithms combine sequential importance sampling, a selection scheme, and Markov chain Monte Carlo methods. They use several variance reduction methods to make the most of the statistical structure of JMLS. Computer simulations are carried out to evaluate the performance of the proposed algorithms. The problems of on-line deconvolution of impulsive processes and of tracking a maneuvering target are considered. It is shown that our algorithms outperform the current methods