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Abstract

A Passive Navigation System (PNS) provides continuous updating of position, velocity, and attitude information of a vehicle without recourse to radiating or external navigation aids. The system accurately computes navigation information with the utilization of gravity sensors, gravimetric maps, vertical position, and velocity measurements. Sensor and map data are optimally processed by real time filtering to compute the best position, velocity, and attitude of the vehicle. The products of measured gravity gradients and the velocity of the vehicle are integrated over time to obtain a north, east, down gravity vector components which are combined with corresponding components obtained from a vertical deflection map in a complementary filter. North and east components of the combination are compared with the corresponding components from the vertical deflection map, while the down component of the gravity is compared to the down value obtained from a gravimeter. Residuals from these comparisons are utilized in a Kalman filter to provide corrections that render inertial measuring units in the system independent of the vertical deflections and gravity anomalies. Measured gravity gradients are compared to reference map gradients, the residuals being utilized in the kalman filter to estimate long term position errors and to provide correction for gradiometer bias and drift. A vertical position loop mixes gravity down data obtained from a gravimeter and gravity down data obtained from the integrator to provide vertical position which is compared to a reference derived from the difference between a measured vehicle height and terrain height obtained from a geoidal map. The residual of this comparison is utilized in the Kalman filter to improve estimates of east velocity.

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First Claim