系统随机噪声对捷联惯导初始对准影响分析(英文)

捷联惯导系统初始对准系统状态方程所描述的状态统计信息,受系统随机噪声驱动,系统随机噪声通过噪声驱动阵作用于状态。常用系统状态方程的系统噪声驱动阵为单位阵。经过分析发现系统噪声驱动阵为单位阵是有条件的;通过推导给出了系统状态方程可以简化此种形式的条件;指出了在水平陀螺、加速度计随机噪声水平不同时,若使用简化系统模型会引起状态估计误差;并通过单轴旋转多位置对准仿真实验进行了验证。仿真结果表明:若水平陀螺随机噪声水平不同,采用简化模型进行初始对准会引起状态估计误差。

Effect of Correlation of Two Dichotomous Noises on Stochastic Resonance

The effect of the correlation of two dichotomous noises on stochastic resonance is investigated for a linear stochastic system subject to a periodic oscillatory signal. It is found that, the correlation between the two dichotomous noises can not only affect the appearance of the stochastic resonance phenomenon, but also the distinctness of the stochastic resonance phenomenon. There is an optimal value of the correlation, at which the stochastic resonance phenomenon is most distinct. In addition, the correlation between the two dichotomous noises can also cause the movement of the peak of stochastic resonance. Finally, two stochastic resonances caused by two correlated multiplicative dichotomous noises can be found in this system.

Stochastic Resonance in an Asymmetric Mono-stable System Subject to Two Periodic Forces and Multiplicative and Additive Noise

The phenomenon of stochastic resonance （SR） in an asymmetric mono-stable system subject to two external periodic forces and multiplicative and additive noise is investigated. It is shown that the signal-to-noise ratio （SNR） for the fundamental and higher harmonics is a non-monotonic function of the intensities of the multiplicative and additive noise, as well as of the system parameter. Moreover, the SNR for the fundamental harmonic decreases with the increase of the system asymmetry, while the SNR for the higher harmonics behaves non-monotonically as the system asymmetry varies.

New sigma point filtering algorithms for nonlinear stochastic systems with correlated noises

New sigma point filtering algorithms, including the unscented Kalman filter （UKF） and the divided difference filter （DDF）, are designed to solve the nonlinear filtering problem under the condition of correlated noises. Based on the minimum mean square error estimation theory, the nonlinear optimal predictive and correction recursive formulas under the hypothesis that the input noise is correlated with the measurement noise are derived and can be described in a unified framework. Then, UKF and DDF with correlated noises are proposed on the basis of approximation of the posterior mean and covariance in the unified framework by using unscented transformation and second order Stirling＇s interpolation. The proposed UKF and DDF with correlated noises break through the limitation that input noise and measurement noise must be assumed to be uneorrelated in standard UKF and DDF. Two simulation examples show the effectiveness and feasibility of new algorithms for dealing with nonlinear filtering issue with correlated noises.

Multiplicative Stochastic Resonance for a Linear System Driven by O-U Noise

In the paper, we study a linear system driven by O-U noise and give a method which is different from the one stated in Europhys. Lett. 40 （1997） 117. We find the same phenomenon of multiplicative stochastic resonance for the response of the system to the signal as the one found in Europhys. Left. 40 （1997） 117. The merit of our method is that it prevents the complex formulas when making sum from n = 0 to n →∞ as in Europhys. Lett. 40 （1997） 117, which leads to the approximate results of the figures.

Stochastic Resonance in a Bacterium Growth System Subjected to Colored Noises

We present the logistic growth model to study the stochastic resonance （SR） in a bacterium growth system under the simultaneous action of two external multiplicative cross-correlation noises and periodic external forcing. The expression of the signal-to-noise ratio （SNR） for a bacterium growth system is derived by using the theory of SNR in the adiabatic limit. Based on SNR, we discuss the effects of self-correlation time τ1 and τ2, cross-correlation time 3-3 and cross-correlation strength λ on the SNR. It is found that the self-correlation time τ1 and τ2, and cross-correlation strength λ enhance the SR of the bacterium growth system, while cross-correlation time τ3 weakens the SR of the bacterium growth system.

Optimal and suboptimal white noise smoothers for nonlinear stochastic systems

A new approach of smoothing the white noise for nonlinear stochastic system was proposed. Through presenting the Gaussian approximation about the white noise posterior smoothing probability density fimction, an optimal and unifying white noise smoothing framework was firstly derived on the basis of the existing state smoother. The proposed framework was only formal in the sense that it rarely could be directly used in practice since the model nonlinearity resulted in the intractability and infeasibility of analytically computing the smoothing gain. For this reason, a suboptimal and practical white noise smoother, which is called the unscented white noise smoother （UWNS）, was further developed by applying unscented transformation to numerically approximate the smoothing gain. Simulation results show the superior performance of the proposed UWNS approach as compared to the existing extended white noise smoother （EWNS） based on the first-order linearization.

Signal modulating noise effect in bistable stochastic resonance systems and its analog simulation

The effect of signal modulating noise in bistable stochastic resonance systems was studied theoretically and experimentally. A mathematical analysis was made on the bistable stochastic resonance model with small system parameters. An analogue circuit was designed to perform the effect. The effect of signal modulating noise was shown in the analog simulation experiment. The analog experiment was conducted for two sinusoidal signals with different frequencies. The results show that there are a sinusoidal component corresponding to the input sinusoidal signal and a noise component presented as a Wiener process corresponding to the input white noise in the system output. By properly selecting system parameters, the effect of signal modulating noise can be manifested in the system output.

ON THE OBSERVABILITY AND DETECTABILITY OF LINEAR STOCHASTIC SYSTEMS WITH MARKOV JUMPS AND MULTIPLICATIVE NOISE

This paper presents the notions of exact observability and exact detectability for Markov jump linear stochastic systems of Ito type with multiplieative noise （for short, MJLSS）. Stochastic Popov-Belevith-Hautus （PBH） Criterions for exact observability and exact detectability are respectively obtained. As an application, stochastic H2/H∞ control for such MJLSS is discussed under exact detectability.

Stochastic stability of viscoelastic system under non-Gaussian colored noise excitation

This article examines a viscoelastic plate that is driven parametrically by a non-Guassian colored noise,which is simplified to an Ornstein-Uhlenbeck process based on the approximation method.To examine the moment stability property of the viscoelastic system,we use the stochastic averaging method,Girsanov theorem and Feynmann-Kac formula to derive the approximate analytic expansion of the moment Lyapunov exponent.Furthermore,the Monte Carlo simulation results for the original system are given to check the accuracy of the approximate analytic results.At the end of this paper,results are presented to show some quantitative pictures of the effects of the system parameters,noise parameters and viscoelastic parameters on the stability of the viscoelastic plate.

Stochastic Resonance in a Simple Threshold Sensor System with Alpha Stable Noise

We investigate the effect of alpha stable noise on stochastic resonance in a single-threshold sensor system by analytic deduction and stochastic simulation. It is shown that stochastic resonance occurs in the threshold system in alpha stable noise environment, but the resonant effect becomes weakened as the alpha stable index decreases or the skewness parameter of alpha stable distribution increases. In particular, for Cauchy noise a nonlinear relation among the optimal noise deviation parameter, the signal amplitude and the threshold is analytically obtained and illustrated by using the extreme value condition for the output signal-to-noise ratio. The results presented in this communication should have application in signal detection and image restoration in the non-Gaussian noisy environment.

Reaching a stochastic consensus in the noisy networks of linear MIMO agents:Dynamic output-feedback and convergence rate

This paper addresses the leader-following consensus problem of linear multi-agent systems(MASs) with communication noise. Each agent's dynamical behavior is described by a linear multi-input and multi-output(MIMO) system, and the agent's full state is assumed to be unavailable. To deal with this challenge, a state observer is constructed to estimate the agent's full state. A dynamic output-feedback based protocol that is based on the estimated state is proposed. To mitigate the effect of communication noise, noise-attenuation gains are also introduced into the proposed protocol. In this study, each agent is allowed to have its own noise-attenuation gain. It is shown that the proposed protocol can solve the mean square leader-following consensus problem of a linear MIMO MAS. Moreover, if all noise-attenuation gains are of Q(t-β), where b∈(0,1), the convergence rate of the MAS can be quantitatively analyzed. It turns out that all followers' states converge to the leader's state in the mean square sense at a rate of O(t-β).

WHITE NOISE ESTIMATION FOR DISCRETE-TIME SYSTEMS WITH RANDOM DELAY AND PACKET DROPOUT

This paper is concerned with the optimal and suboptimal deconvolution problems for discrete-time systems with random delayed observations. When the random delay is known online, i.e., time stamped, the random delayed system is reconstructed as an equivalent delay-free one by using measurement reorganization technique, and then an optimal input white noise estimator is presented based on the stochastic Kahnan filtering theory. However, tb_e optimal white-noise estimator is timevarying, stochastic, and doesn＇t converge to a steady state in general. Then an alternative suboptimal input white-noise estimator with deterministic gains is developed under a new criteria. The estimator gain and its respective error covariance-matrix information are derived based on a new suboptimal state estimator. It can be shown that the suboptimal input white-noise estimator converges to a steady-state one under appropriate assumptions.