Parametric estimation of ultra wideband radar targets

Based on the analysis of impulse response properties, a scattering model of ultra wideband （UWB） radar targets is developed to estimate the target parameters exactly. With this model, two algorithms of multiple signal classification （MUSIC）, and matrix pencil （MP）, are introduced to calculate the scattering center parameters of targets and their performances are compared. The simulation experiments show that there are no differences in the estimation precision of MUSIC and MP methods when the signal-to-noise ratio （SNR） is larger than 13 dB. However, the MP method has a better performance than that of MUSIC method when the SNR is smaller than 13 dB. Besides, the time consuming of MP method is less than that of MUSIC method. Therefore, the MP algorithm is preferred for the parametric estimation of UWB radar targets.

NON-PARAMETRIC ESTIMATION IN CONTAMINATED LINEAR MODEL

In this paper, the following contaminated linear model is considered:y i=(1-ε)x τ iβ+z i, 1≤i≤n,where r.v.'s ｛ y i ｝ are contaminated with errors ｛ z i ｝. To assume that the errors have the finite moment of order 2 only. The non parametric estimation of contaminated coefficient ε and regression parameter β are established, and the strong consistency and convergence rate almost surely of the estimators are obtained. A simulated example is also given to show the visual performance of the estimations.

Parametric Estimation of Interconnected Nonlinear Systems Described by Input-output Mathematical Models

In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be described by an input-output nonlinear discrete-time mathematical model, with unknown, but constant or slowly time-varying parameters. Then, two recursive estimation methods are used to solve the parametric estimation problem for the considered class of the interconnected nonlinear systems. These methods are based on the recursive least squares techniques and the prediction error method. Convergence analysis is provided using the hyper-stability and positivity method and the differential equation approach. A numerical simulation example of the parametric estimation of a stochastic interconnected nonlinear hydraulic system is treated.

Topp-Leone Odd Fréchet Generated Family of Distributions with Applications to COVID-19 Data Sets

Recent studies have pointed out the potential of the odd Fréchet family(or class)of continuous distributions in fitting data of all kinds.In this article,we propose an extension of this family through the so-called“Topp-Leone strategy”,aiming to improve its overall flexibility by adding a shape parameter.The main objective is to offer original distributions with modifiable properties,from which adaptive and pliant statistical models can be derived.For the new family,these aspects are illustrated by the means of comprehensive mathematical and numerical results.In particular,we emphasize a special distribution with three parameters based on the exponential distribution.The related model is shown to be skillful to the fitting of various lifetime data,more or less heterogeneous.Among all the possible applications,we consider two data sets of current interest,linked to the COVID-19 pandemic.They concern daily cases confirmed and recovered in Pakistan from March 24 to April 28,2020.As a result of our analyzes,the proposed model has the best fitting results in comparison to serious challengers,including the former odd Fréchet model.

Intelligent Cost Modeling Based on Soft Computing for Avionics Systems

In parametric cost estimating, objections to using statistical Cost Estimating Relationships （CERs） and parametric models include problems of low statistical significance due to limited data points, biases in the underlying data, and lack of robustness. Soft Computing （SC） technologies are used for building intelligent cost models. The SC models are systemically evaluated based on their training and prediction of the historical cost data of airborne avionics systems. Results indicating the strengths and weakness of each model are presented. In general, the intelligent cost models have higher prediction precision, better data adaptability, and stronger self-learning capability than the regression CERs.

Application of a hybrid algorithm ePSOSA in well test parameter estimation

Estimating the significance parameters,such as skin factor,permeability,wellbore storage coefficient,are the most component of transient pressure analysis.Many optimization algorithms have been applied to parametric estimation and realized the minimum error of well test curve.Although a flexible heuristic particle swarm optimization can hunt optimal solution rapidly,it is difficult to search further in the vicinity of the optimal solution.Hence,to alleviate the local optimum and premature convergence,a global hybrid algorithm referred to as particle swarm simulated annealing is proposed,and proves to have better performance of convergence and accuracy than traditional methods,which are more suitable for parameter estimation.