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A common drain circuit is shown in Fig 23-13 In this circuit, the collector is at signal ground It is sometimes called a source follower The FET is biased in the same way as for the common source and common gate circuits In the illustration, an N-channel JFET is shown, but any other kind of FET could be used, reversing the polarity for P-channel devices Enhancement-mode MOSFETs would need a resistor between the gate and the positive supply terminal (or the negative terminal if the MOSFET is P-channel) The input signal passes through C 2 to the gate Resistors R1 and R2 provide gate bias Resistor R3 limits the current Capacitor C3 keeps the drain at signal ground Fluctuating dc (the channel current) flows through R1 as a result of the input signal; this causes a fluctuating dc voltage to appear across R1 The output is taken from the source, and its ac component passes through C1 The output of the common drain circuit is in phase with the input This scheme is the FET analog of the bipolar common collector arrangement The output impedance is rather low, making this circuit a good choice for broadband impedance matching

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Even for this simple application, both approaches implement an EKF due to the nonlinearity imposed by the scale factor error In the approach of Section 71 the nonlinearity is induced by the accelerometer scale factor compensation in the time update In the approach of Section 74 the nonlinearity appears in the accelerometer portion of the measurement update In more realistic applications, the kinematics typically involve nonlinearities, so an EKF will be required in either approach In the approach of Section 71, there are no free tuning parameters All the parameter values are determined from the characteristics of the instruments Based on the results of observability, covariance, or simulation analysis, the designer may choose to combine or remove states or to tune the parameters determined from the sensor characteristics; however, there is a rigorous basis for the initial settings and subsequent tuning The basic design model is a Markov process driven by stationary white noise processes and therefore satis es the derivation assumptions for the Kalman lter The approach of Section 74 starts with a model hypothesis for the acceleration state In most applications, there is no time invariant Markov model for this state because the signal characteristics are determined by the end user and usually cannot be reliably predicted during the system design phase The design does involve free tuning parameters and does not satisfy the basic modeling assumptions of the Kalman lter Because both approaches implement EKF s the P matrix is not, strictly speaking, the error covariance matrix in either case However, as long as x is kept small, in the approach of Section 71 the linearization error is small, and P is typically a reasonable representation of the accuracy of x In the case of the approach of Section 74 the matrix P cannot be expected to be a reasonable characterization of the accuracy of x unless the assumed model for the acceleration turns out to be accurate Similarly, the EKF measurement update in Section 71 is the optimal linear correction The EKF measurement update in Section 74 is only optimal if the assumed model for the acceleration turns out to be accurate The computational load of aided navigation systems is typically dominated by the time update of the P matrix For the two algorithms, the computational load for the P matrix time and measurement propagation over each second of operation are compared in the Table 72 Eqn (721).

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