INFLUENCE OF STOCHASTIC NOISE STATISTICS ON KALMAN FILTER PERFORMANCE BASED ON VIDEO TARGET TRACKING

The system stochastic noises involved in Kalman filtering are preconditioned on being ideally white and Gaussian distributed. In this research, efforts are exerted on exploring the influence of the noise statistics on Kalman filtering from the perspective of video target tracking quality. The correlation of tracking precision to both the process and measurement noise covariance is investigated; the signal-to-noise power density ratio is defined; the contribution of predicted states and measured outputs to Kalman filter behavior is discussed; the tracking precision relative sensitivity is derived and applied in this study case. The findings are expected to pave the way for future study on how the actual noise statistics deviating from the assumed ones impacts on the Kalman filter optimality and degra-dation in the application of video tracking.

De-Noising Stochastic Noise in FOG Based on Second-Generation DB4 Wavelet and SURE-Threshold

An effective de-noising method for fiber optic gyroscopes （FOGs） is proposed. This method is based on second-generation Daubechies D4 （DB4） wavelet transform （WT） and level-dependent threshold estimator called Stein＇s unbiased risk estimator （SURE）. The whole approach consists of three critical parts： wavelet decomposition module, parameters estimation module and SURE de-noising module. First, DB4 wavelet is selected as lifting base of the second-generation wavelet in the decomposition module. Second, in the parameters estimation module, maximum likelihood estimation （MLE） is used for stochastic noise parameters estimation. Third, combined with soft threshold de-noising technique, the SURE de-noising module is designed. For comparison, both the traditional universal threshold wavelet and the second-generation Harr wavelet method are also investigated. The experiment results show that the computation cost is 40% less than that of the traditional wavelet method. The standard deviation of de-noised FOG signal is 0.012 and the three noise terms such as angle random walk, bias instability and quantization noise are reduced to 0.007 2°/√h, 0.004 1° / h, and 0.008 1°, respectively.

Time series modeling and filtering method of electric power load stochastic noise

Stochastic noises have a great adverse effect on the prediction accuracy of electric power load.Modeling online and filtering real-time can effectively improve measurement accuracy.Firstly,pretreating and inspecting statistically the electric power load data is essential to characterize the stochastic noise of electric power load.Then,set order for the time series model by Akaike information criterion(AIC)rule and acquire model coefficients to establish ARMA(2,1)model.Next,test the applicability of the established model.Finally,Kalman filter is adopted to process the electric power load data.Simulation results of total variance demonstrate that stochastic noise is obviously decreased after Kalman filtering based on ARMA(2,1)model.Besides,variance is reduced by two orders,and every coefficient of stochastic noise is reduced by one order.The filter method based on time series model does reduce stochastic noise of electric power load,and increase measurement accuracy.

Stochastic bifurcations of generalized Duffing–van der Pol system with fractional derivative under colored noise

The stochastic bifurcation of a generalized Duffing–van der Pol system with fractional derivative under color noise excitation is studied. Firstly, fractional derivative in a form of generalized integral with time-delay is approximated by a set of periodic functions. Based on this work, the stochastic averaging method is applied to obtain the FPK equation and the stationary probability density of the amplitude. After that, the critical parameter conditions of stochastic P-bifurcation are obtained based on the singularity theory. Different types of stationary probability densities of the amplitude are also obtained. The study finds that the change of noise intensity, fractional order, and correlation time will lead to the stochastic bifurcation.

Influences of noise correlation and time delay on stochastic resonance induced by multiplicative signal in a cancer growth system

This paper investigates the stochastic resonance （SR） phenomenon induced by the multiplicative periodic signal in a cancer growth system with the cross-correlated noises and time delay. To describe the periodic change of the birth rate due to the periodic treatment, a multiplicative periodic signal is added to the system. Under the condition of small delay time, the analytical expression of the signal-to-noise ratio RSNR is derived in the adiabatic limit. By numerical calculation, the effects of the cross-correlation strength λ and the delay time τ on RSNR are respectively discussed. The existence of a peak in the curves of RSNR as a function of the noise intensities indicates the occurrence of the SR phenomenon. It is found that λ and τ play opposite role on the SR phenomenon, i.e., the SR is suppressed by increasing λ whereas it is enhanced with the increase of τ, which is different from the case where the periodic signal is additive.

Langevin approach with rescaled noise for stochastic channel dynamics in Hodgkin–Huxley neurons

The Langevin approach has been applied to model the random open and closing dynamics of ion channels. It has long been known that the gate-based Langevin approach is not sufficiently accurate to reproduce the statistics of stochastic channel dynamics in Hodgkin–Huxley neurons. Here, we introduce a modified gate-based Langevin approach with rescaled noise strength to simulate stochastic channel dynamics. The rescaled independent gate and identical gate Langevin approaches improve the statistical results for the mean membrane voltage, inter-spike interval, and spike amplitude.

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with general sampling delay

In this paper we provide a unified framework for consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with a general sampling delay. First, a stochastic bounded consensus tracking protocol based on sampled data with a general sampling delay is presented by employing the delay decomposition technique. Then, necessary and sufficient conditions are derived for guaranteeing leader-follower multi-agent systems with measurement noises and a time-varying reference state to achieve mean square bounded consensus tracking. The obtained results cover no sampling delay, a small sampling delay and a large sampling delay as three special cases. Last, simulations are provided to demonstrate the effectiveness of the theoretical results.

Polynomial networks based adaptive attitude tracking control for NSVs with input constraints and stochastic noises

This paper proposes a backstepping technique and Multi-dimensional Taylor Polynomial Networks(MTPN)based adaptive attitude tracking control strategy for Near Space Vehicles(NSVs)subjected to input constraints and stochastic input noises.Firstly,considering the control input has stochastic noises,and the attitude motion dynamical model of the NSVs is actually modeled as the Multi-Input Multi-Output(MIMO)stochastic nonlinear system form.Furthermore,the MTPN is used to estimate the unknown system uncertainties,and an auxiliary system is designed to compensate the influence of the saturation control input.Then,by using backstepping method and the output of the auxiliary system,a MTPN-based robust adaptive attitude control approach is proposed for the NSVs with saturation input nonlinearity,stochastic input noises,and system uncertainties.Stochastic Lyapunov stability theory is utilized to analysis the stability in the sense of probability of the entire closed-loop system.Additionally,by selecting appropriate parameters,the tracking errors will converge to a small neighborhood with a tunable radius.Finally,the numerical simulation results of the NSVs attitude motion show the satisfactory flight control performance under the proposed tracking control strategy.

3D NUMERICAL SOLUTION OF AERO-NOISE WITH HIGH-ORDER DISCONTINUOUS GALERKIN METHOD

The flow-induced noise is simulated with a hybrid method.Firstly,a steady-state background flow field is given by solving Reynolds averaged Navier-Stokes(RANS)equations with finite volume(FV)method on structured grid.Then the linearized Euler equations(LEE)can be constructed based on the resulted background flow field,where the source term on the right hand side is computed using stochastic noise generation and radiation(SNGR)method.Finally,the unsteady acoustic field is obtained through solving LEE using high-order discontinuous Galerkin(DG)method on unstructured grid,where the parallel computing based on mesh partitioning and a″Quadrature-Free Implementation″method for high-order DG are employed to accelerate the computation.In order to demonstrate the sound propagation in detail,a visualization method for high-order schemes is also developed here.Moreover,in order to test the validation and the accuracy,a 3D cavity test in comparison with the experimental data is displayed first in this paper,then a 3D high-lift wing is also simulated to demonstrate its capability for very complex geometries.

Stochastic responses of tumor-immune system with periodic treatment

We investigate the stochastic responses of a tumor–immune system competition model with environmental noise and periodic treatment. Firstly, a mathematical model describing the interaction between tumor cells and immune system under external fluctuations and periodic treatment is established based on the stochastic differential equation. Then, sufficient conditions for extinction and persistence of the tumor cells are derived by constructing Lyapunov functions and Ito＇s formula. Finally, numerical simulations are introduced to illustrate and verify the results. The results of this work provide the theoretical basis for designing more effective and precise therapeutic strategies to eliminate cancer cells, especially for combining the immunotherapy and the traditional tools.

Dynamics of a prey-predator system under Poisson white noise excitation

The classical Lotka-Volterra （LV） model is a well-known mathematical model for prey-predator ecosystems. In the present paper, the pulse-type version of stochastic LV model, in which the effect of a random natural environment has been modeled as Poisson white noise, is in- vestigated by using the stochastic averaging method. The averaged generalized It6 stochastic differential equation and Fokkerlanck-Kolmogorov （FPK） equation are derived for prey-predator ecosystem driven by Poisson white noise. Approximate stationary solution for the averaged generalized FPK equation is obtained by using the perturbation method. The effect of prey self-competition parameter e2s on ecosystem behavior is evaluated. The analytical result is confirmed by corresponding Monte Carlo （MC） simulation.

STATIONARY OSCILLATIONS OF SECOND ORDER PERIODIC SYSTEMS UNDER STOCHASTIC NOISE INTERFERENCE

In this paper,the existence of stationary oscillations of second order periodic systemsunder stochastlc noise interference is investigated,and the necessary and sufficient condition for the existence of stationary oscillations in the sense of mean-square stability is obtained via establishing equivalent vector stochastic systems and establishing deterministicequivalent systems in the sense of stability, and applying the existence theorem of stationary oscillations for the deterministic systems.