Nonlinear Filters. Simon Haykin

Nonlinear Filters - Simon  Haykin


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k Baseline equals Start 1 By 2 Matrix 1st Row 1st Column bold upper C overTilde Superscript o Baseline 2nd Column bold 0 EndMatrix StartBinomialOrMatrix bold z Subscript k Superscript o Baseline Choose bold z Subscript k Superscript o overbar Baseline EndBinomialOrMatrix plus bold upper D bold u Subscript k Baseline period"/>

      2.4.3 Discretization of LTI Systems

      When a continuous‐time system is connected to a computer via analog‐to‐digital and digital‐to‐analog converters at input and output, respectively, we need to find a discrete‐time equivalent of the continuous‐time system that describes the relationship between the system's input and its output at certain time instants (sampling times t Subscript k for k equals 0 comma 1 comma ellipsis). This process is called sampling the continuous‐time system. Using zero‐order‐hold sampling, where the corresponding analog signals are kept constant over the sampling period, we will have the following discrete‐time equivalent for the continuous‐time system of (2.3) and (2.4) [18]:

      (2.32)StartLayout 1st Row 1st Column bold x left-parenthesis t Subscript k plus 1 Baseline right-parenthesis 2nd Column equals bold upper Phi left-parenthesis t Subscript k plus 1 Baseline comma t Subscript k Baseline right-parenthesis bold x left-parenthesis t Subscript k Baseline right-parenthesis plus bold upper Gamma left-parenthesis t Subscript k plus 1 Baseline comma t Subscript k Baseline right-parenthesis bold u left-parenthesis t Subscript k Baseline right-parenthesis comma EndLayout

      (2.33)StartLayout 1st Row 1st Column bold y left-parenthesis t Subscript k Baseline right-parenthesis 2nd Column equals bold upper C bold x left-parenthesis t Subscript k Baseline right-parenthesis plus bold upper D bold u left-parenthesis t Subscript k Baseline right-parenthesis comma EndLayout

      where

      (2.34)StartLayout 1st Row 1st Column bold upper Phi left-parenthesis t Subscript k plus 1 Baseline comma t Subscript k Baseline right-parenthesis 2nd Column equals normal e Superscript bold upper A left-parenthesis t Super Subscript k plus 1 Superscript minus t Super Subscript k Superscript right-parenthesis Baseline comma EndLayout

      (2.35)StartLayout 1st Row 1st Column bold upper Gamma left-parenthesis t Subscript k plus 1 Baseline comma t Subscript k Baseline right-parenthesis 2nd Column equals integral Subscript 0 Superscript t Subscript k plus 1 Baseline minus t Subscript k Baseline Baseline normal e Superscript bold upper A tau Baseline normal d tau bold upper B period EndLayout

      If the system matrices in the state‐space model of a linear system change with time, then, the model represents a linear time‐varying (LTV) system. Obviously, the observability condition would be more complicated for LTV systems compared to LTI systems.

      2.5.1 Continuous‐Time LTV Systems

      The state‐space model of a continuous‐time LTV system is represented by the following algebraic and differential equations:

      (2.38)ModifyingAbove bold x With dot left-parenthesis t right-parenthesis equals bold upper A left-parenthesis t right-parenthesis bold x left-parenthesis t right-parenthesis comma

      whose solution is:

      where bold upper Phi left-parenthesis t comma t 0 right-parenthesis is called the continuous‐time state‐transition matrix, which is itself the solution of the following differential equation:

      with the initial condition:

      Note that for an LTI system, where the matrix bold upper A is constant, the state transition matrix will be:

      (2.42)bold upper Phi left-parenthesis t comma t 0 right-parenthesis equals normal e Superscript bold upper A left-parenthesis t minus t 0 right-parenthesis Baseline period

      (2.43)bold y left-parenthesis t right-parenthesis equals <hr><noindex><a href=Скачать книгу