دانلود کتاب تخمین حالت بهینه و قوی – پاسخ تکانه محدود (FIR) و رویکردهای کالمن

The state estimation approach arose fromthe need to know the internal state of a real system, given that the input and output measurements are known. The corresponding structure is called a state estimator, and in control theory it is also called a state observer. In signal processing, the problem is related to the process state and its transition from one point to another. In contrast to parameter estimation theory, which deals with the estimation of the parameters of the fitting function, the state estimation approach is more suitable for engineering applications and the development of end-to-end algorithms. Knowing the system state helps to solve many engineering problems. In systems, the state usually cannot be observed directly, but its indirect observation can be provided by way of the system outputs. In control, it is used to stabilize a system via state feedback. In signal processing, the direct, inverse, and identification problems are solved by applying state estimators (filters, smoothers, and predictors) to linear and nonlinear processes. In biomedical applications, state estimators facilitate extracting required process features.