Tracking and Kalman filtering made easy P16

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Tracking and Kalman filtering made easy P16

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NONLINEAR OBSERVATION SCHEME AND DYNAMIC MODEL (EXTENDED KALMAN FILTER) 16.1 INTRODUCTION In this section we extend the results for the linear time-invariant and timevariant cases to where the observations are nonlinearly related to the state vector and/or the target dynamics model is a nonlinear relationship [5, pp. 105– 111, 166–171, 298–300]. The approachs involve the use of linearization procedures. This linearization allows us to apply the linear least-squares and minimum-variance theory results obtained so far. When these linearization procedures are used with the Kalman filter, we obtain what is called the extended Kalman filter [7, 122]. 16.2 NONLINEAR OBSERVATION SCHEME When the...

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