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A very simple dead-reckoning navigation system can be designed by inte gration of xo = f (xo , y) for t to from some initial xo (to ) where xo = [no , eo , o ] The resulting dead-reckoning system state, xo (t) for t 0, will be used as the reference trajectory for the linearization process The navigation system equation can be simpli ed as follows: xo = f (xo , y) u cos( o ) eu cos( o ) (u + eu ) cos( o ) = (u + eu ) sin( o ) = u sin( o ) + eu sin( o ) ( + e ) e eu cos( o ) = f (xo , u) + eu sin( o ) (337) e
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The rst order (ie, linearized) dynamics of the error between the actual and navigation states are 0 0 sin( o ) u cos( o ) 0 eu u cos( o ) v sin( o ) 0 v= 0 0 (338) e 0 0 0 0 1
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where u = y1 and v = [ n, e, ] = x xo To derive this linearized model, the function f in eqn (336) is expanded using Taylor series Then eqn (337) is subtracted from the Taylor series expansion of eqn (336) to yield eqn (338) For additional discussion related to this example, see 9 Error state dynamic equations enable quantitative analysis of the error state itself Later, the linearized error state equations will be used in the design of Kalman lters to estimate the error state Prior to that, we must discuss such issues as stochastic modeling of sensor errors which is done in 4
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(12)
when a bias estimate is available For this simple example, = 0 Sumb b marizing the results of the above analysis, the model of the time variation of the navigation error is p v = v = a a (13)
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