Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí hóa hoc quốc tế đề tài : A general solution to the continuous-time estimation problem under widely linear processing | Martínez-Rodríguez et al. EURASIP Journal on Advances in Signal Processing 2011 2011 119 ý EURASIP Journal on http content 2011 1 119 Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access A general solution to the continuous-time estimation problem under widely linear processing Ana María Martínez-Rodríguez Jesus Navarro-Moreno Rosa María Fernandez-Alcala and Juan Carlos Ruiz-Molina Abstract A general problem of continuous-time linear mean-square estimation of a signal under widely linear processing is studied. The main characteristic of the estimator provided is the generality of its formulation which is applicable to a broad variety of situations including finite or infinite intervals different types of noises additive and or multiplicative white or colored noiseless observation data etc. capable of solving three estimation problems smoothing filtering or prediction and estimating functionals of the signal of interest derivatives integrals etc. . Its feasibility from a practical standpoint and a better performance with respect to the conventional estimator obtained from strictly linear processing is also illustrated. Keywords Continuous-time processing Linear mean-square estimation problem Widely linear processing 1 Introduction In most engineering systems the state variables represent some physical quantity that is inherently continuous in time ground-motion parameters atmospheric or oceanographic flow and turbulence etc. . Thus the formulation of realistic models to represent a signal processing problem is one of the major challenges facing engineers and mathematicians today. Given that in many problems the incoming information is constituted by continuous-time series the use of a continuous-time model will be a more realistic description of the underlying phenomena we are trying to model. For example 1 gives techniques of continuous-time linear system identification and 2 illustrates the use of stochastic .
