scholarly journals Method of description for the dynamics of the signal delay change in discrete time with changing aircraft position in space

2021 ◽  
pp. 41-45
Author(s):  
А.О. Красноруцький ◽  
О.В. Федоровський ◽  
В.Ж. Ященок
Keyword(s):  
White Noise ◽  
Discrete Time ◽  
Dimensional Space ◽  
Dynamic Change ◽  
Flight Simulation ◽  
Spatial Position ◽  
Signal Delay ◽  
Simulation Data ◽  
Aerodynamic Properties ◽  
Range Parameter

The article presents an innovative approach to the description of the aircraft range parameter in discrete time when simulating the process of its repositioning in space. A method of describing the dynamics of changes in signal delay in discrete time when the aircraft is changing its spatial position is proposed. Such a model adequately describes the change in signal delay in discrete time. The direction of estimation for the adequacy of radio signal delay change simulation in algorithms of optimum filtration is defined in accordance with the aerodynamic properties of the aircraft. Simulation of the signal delay dynamic change is carried out (it is completely described by the dynamics of change in the distance to the aircraft). The transformation stages of simulation data for the initial model in continuous time with realization of the standard Gaussian random numbers are justified. Information on the simulation data transformation taking into account the correlation matrix of discrete white noise is provided. A method of calculating the transition matrix through the Laplace transform is proposed. The scientific-applied direction of research is determined – it lies in the development of a method for the legitimate representation for the mathematical model of aircraft’s changing range in discrete time within one-dimensional space: the longitudinal and the transverse dimensions. This approach takes into account the continuous description for a system of stochastic differential equations. A comprehensive algorithm for modeling the values of discrete white noise on modern computer equipment and calculating the dynamics of the aircraft range parameter changes is proposed. This algorithm allows to correctly form the a priori information about the change of the vector parameters of the aircraft spatial position at discrete moments of time. As a result, it was shown that the use of the obtained information in the optimal filtering algorithms minimizes the error when determining distance to the aircraft and, accordingly, allows to increase the accuracy and adequacy of the signal delay simulation in discrete time. The results of this research can be used in modernization of the existing models and development of promising on-board radar stations, integrated rangefinders, systems of radio technical reconnaissance and electronic warfare systems, as well as in technical implementation of aircraft flight simulation systems.

Time Series Methods for the Synthesis of Random Vibration Systems

1976 ◽  
Vol 43 (1) ◽  
pp. 159-165 ◽  
Author(s):  
W. Gersch ◽  
R. S-Z. Liu
Keyword(s):  
Differential Equation ◽  
Time Series ◽  
White Noise ◽  
Least Squares ◽  
Discrete Time ◽  
Covariance Function ◽  
Structural System ◽  
Covariance Functions ◽  
Discrete Time Series ◽  
Scalar Output

A least-squares method procedure for synthesizing the discrete time series that is characteristic of the uniform samples of the response of linear structural systems to stationary random excitation is described. The structural system is assumed to be an n-degree-of-freedom system that is representable by a set of ordinary differential equations excited by a vector white noise force. It is known that the discrete time series of uniformly spaced samples of a scalar white noise excited stationary linear differential equation can be represented as an autoregressive-moving average (AR-MA) time series and that the parameters of the AR-MA model can be computed from the covariance function of the differential equation model. The contributions of this paper are (i) the result that a scalar input scalar output AR-MA model duplicates the scalar output covariance function of a regularly sampled linear structural system with a multivariate white noise input, (ii) a computationally efficient method for computing the covariance function of a randomly excited structural system, and (iii) a demonstration of the theory and the numerical details of a two-stage least-squares procedure for the computation of the AR-MA parameters from the output covariance functions data.

scholarly journals Filtering with a limiter

1998 ◽  
Vol 11 (3) ◽  
pp. 289-300 ◽  
Author(s):  
R. Liptser ◽  
P. Muzhikanov
Keyword(s):  
Weak Convergence ◽  
Diffusion Process ◽  
White Noise ◽  
Discrete Time ◽  
Diffusion Approximation ◽  
Conditional Expectation ◽  
Gaussian White Noise ◽  
Sampling Step ◽  
Non Gaussian ◽  
Filtering Problem

We consider a filtering problem for a Gaussian diffusion process observed via discrete-time samples corrupted by a non-Gaussian white noise. Combining the Goggin's result [2] on weak convergence for conditional expectation with diffusion approximation when a sampling step goes to zero we construct an asymptotic optimal filter. Our filter uses centered observations passed through a limiter. Being asymptotically equivalent to a similar filter without centering, it yields a better filtering accuracy in a prelimit case.

The exact bispectra for bilinear realizable processes with hermite degree 2

1991 ◽  
Vol 23 (04) ◽  
pp. 798-808 ◽  
Author(s):  
György Terdik ◽  
Laurie Meaux
Keyword(s):  
White Noise ◽  
Discrete Time ◽  
Hermite Polynomial ◽  
Transfer Functions ◽  
Gaussian White Noise ◽  
Second Order ◽  
Bilinear Model ◽  
White Noise Process ◽  
Noise Process ◽  
Gaussian White Noise Process

This paper deals with the stationary bilinear model with Hermite degree 2 in discrete time which is built up by the first- and second-order Hermite polynomial of a Gaussian white noise process. The exact spectrum and bispectrum is constructed in terms of the transfer functions of the model.

The exact bispectra for bilinear realizable processes with hermite degree 2

1991 ◽  
Vol 23 (4) ◽  
pp. 798-808 ◽  
Author(s):  
György Terdik ◽  
Laurie Meaux
Keyword(s):  
White Noise ◽  
Discrete Time ◽  
Hermite Polynomial ◽  
Transfer Functions ◽  
Gaussian White Noise ◽  
Second Order ◽  
Bilinear Model ◽  
White Noise Process ◽  
Noise Process ◽  
Gaussian White Noise Process

This paper deals with the stationary bilinear model with Hermite degree 2 in discrete time which is built up by the first- and second-order Hermite polynomial of a Gaussian white noise process. The exact spectrum and bispectrum is constructed in terms of the transfer functions of the model.

Simulation-Based Helicopter Mission Effectiveness Evaluation Method

2014 ◽  
Vol 651-653 ◽  
pp. 962-965
Author(s):  
Ji Cai ◽  
Hu Liu ◽  
Hong Tang
Keyword(s):  
Evaluation Method ◽  
Weight Coefficient ◽  
Flight Simulation ◽  
Effectiveness Evaluation ◽  
Simulation Data ◽  
Advantages And Disadvantages ◽  
Helicopter Flight ◽  
Parameter Evaluation ◽  
Simulation Based ◽  
Mission Effectiveness

It is always difficult to obtain a reliable result of helicopter mission effectiveness evaluation (HMEE) by traditional methods. In order to solve the problem, this paper compared and analysed advantages and disadvantages of traditional effectiveness evaluation methods, and presented a simulation-based helicopter mission effectiveness evaluation method. The method is based on the simulation data. The method combines with advantages of multi parameter evaluation method, computer simulation evaluation method and logarithmic evaluation method. By using the reliable helicopter flight simulation data and an objective weight coefficient calculation methods, we can get accurate and reliable helicopter mission effectiveness value. Research proves that this method can be applied in the field of effectiveness evaluation.

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