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Figure 117: Lever Arm Compensation Position Aiding Consider the hypothetical aiding sensor that provides a measurement that is linearly related to the position of the aiding sensor ya = h pn + a (11167)

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The navigation system predicts this output by combining eqns (11166) and (11167), ya a = h pn n b r = h pn + Rn b (11168) (11169)

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n where pn and Rn are directly computable from the state vector of the b navigation system, and b is the speci ed value of the o set vector Ideally, r b would equal the actual value rb , but error rb = rb b between the two r r may exist for the reasons stated previously The residual aiding error, used to drive the Kalman lter, is computed as ya This residual error is modeled as ya = = r n h pn pn + Rn rb Rn b + n b b h ( pn [ n ] ) + Rn rb + r n b (11171) (11172) = y a ya (11170)

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r where n = Rn b The nal equation provides a linear relationship, suitable r b for Kalman lter processing, between the position measurement residual and the INS error states ( pn and ) The term containing rb represents n the error in the measurement due to error in calibration of the lever arm rb This e ect is time varying due to the matrix Rn changing as the b vehicle maneuvers and due to possible time variation in rb itself Velocity Aiding Assume that an aiding sensor is available that outputs a measurement linearly related to velocity of the aiding system

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