Passive navigation system

• US 6,014,103 A
• Filed: 03/05/1999
• Issued: 01/11/2000
• Est. Priority Date: 05/29/1997
• Status: Expired due to Term

• Current Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Original Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Inventors: Dmytrasz, Joseph N., Gatta, Joseph, Ritchie, Henry, Sumner, Donald L., Chew, Sung-Leung
• Primary Examiner(s): Blum, Theodore M.
• Application Number:
  US09/263,044
• Time in Patent Office: 312 Days
• Field of Search: 342/457
• US Class Current: 342/457
• CPC Class Codes: G01C 21/185 for gravity
• CPC Class Codes: G01C 21/188 for accumulated errors, e.g. by coupling inertial systems with absolute positioning systems

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.

Claims

1. An apparatus for navigating a vehicle, of the type having a gravity sensor, a gravity gradiometer, gravity reference map memory storing geographic gravity data, a vehicle position and velocity sensor, and an optimum filter which utilizes data supplied to provide a state vector, the components of which are used to update navigational data comprising:

an integrator, coupled to said vehicle position and velocity sensor and to said gravity gradiometer, to provide signals representative of an integration, over a selected time interval, of a product of gravity gradient and vehicle velocity, thereby providing output signals representative of a gravity vector having a gravity north component, a gravity east component, and a gravity down component;

a gravity vertical deflection map memory, storing geographic gravity vertical deflection data;

a complementary filter, coupled said gravity vertical deflection memory and to said integrator, to provide signals representative of gravity vertical deflection estimates;

a first comparator coupled to said complementary filter, to said gravity vertical deflection map memory, and to said optimum filter to provide signals representative of differences between gravity vertical deflection read from said gravity vertical deflection map memory and gravity vertical deflection established by said complementary filter; and

a correction processor coupled to said optimum filter for updating position, velocity, and attitude of said vehicle.

2. The navigation apparatus of claim 1 wherein said gravity gradiometer provides a measured gravity gradient matrix and said gravity reference map memory provides a reference gravity gradient matrix and further comprising a fifth comparator coupled to said gravity gradiometer, said gravity reference map memory, and said optimum filter to provide signals respectively representative of differences between corresponding components of said measured gravity matrix and said reference gravity gradient matrix, said differences being utilized in the optimum filter to determine long term position errors and to correct gravity gradiometer bias and drift.

3. The navigation apparatus of claim 1 further comprising a second comparator coupled to said gravity sensor, said integrator, and said optimum filter for providing signals representative of differences between said gravity down components determined by said gravity sensor and gravity down components provided by said integrator from which east velocity correction components are established in said state vector.

4. The navigation apparatus of claim 1 further comprising a vertical deflection corrector coupled between said first comparator and said optimum filter, and further coupled to said vehicle position and velocity sensor for providing signals representative of speed corrected residual vertical deflections.

5. The navigation apparatus of claim 1 further comprising a second comparator coupled to said gravity sensor, said integrator, and said optimum filter for providing signals representative of differences between said gravity down components determined by said gravity sensor and gravity down components provided by said integrator from which east velocity correction components are established in said state vector.

6. The navigation apparatus of claim 5 further comprising:

vertical position indicator coupled to said second comparator to provide a signal representative of vertical position of said vehicle;

a height sensor for providing signals representative of vehicle vertical position;

a geoidal height map memory for providing signals representative of terrain height;

a third comparator coupled to said height sensor and said geoidal height map memory to provide signals representative of differences between said vehicle vertical position and said terrain height, thereby providing vehicle vertical position above terrain;

a fourth comparator coupled to said vertical position indicator, to said third comparator, and to said optimum to provide signals representative of differences between said vehicle vertical position above terrain and said vehicle vertical position (Δ

H) from which a second east velocity correction component in said state vector is determined.

7. The navigation apparatus of claim 1 wherein said gravity gradiometer provides a measured gravity gradient matrix and said gravity reference map memory provides a reference gravity gradient matrix and further comprising a second comparator coupled to said gravity gradiometer, said gravity reference map memory, and said optimum filter to provide signals respectively representative of differences between corresponding components of said measured gravity matrix and said reference gravity gradient matrix, said differences being utilized in the optimum filter to determine long term position errors and to correct gravity gradiometer bias and drift.

8. The navigation apparatus of claim 1 wherein said gravity reference map memory provides signals representative of reference gravity anomalies and said gravity sensor provides signals representative of measured gravity anomalies and further comprising a second comparator coupled to said gravity reference map memory, said gravity sensor, and said optimum filter for providing signals representative of differences between said measured gravity anomalies and said reference gravity anomalies from which vehicle position errors are determined.

9. An apparatus for navigating a vehicle, the apparatus being of the type having gravity gradiometer for determining gravity gradients, gravity reference map memory which stores geographic gravity data, and a optimum filter which utilizes data supplied to provide a state vector, the components of which are used to update navigation data

wherein said gravity gradiometer provides a measured gravity gradient matrix and said gravity reference map memory provides a reference gravity gradient matrix;

and further comprising;

a first comparator coupled to said gravity gradiometer, said gravity reference map memory, and said optimum filter for providing signals respectively representative of differences between corresponding components of said measured gravity matrix and said reference gravity gradient matrix from which signals representative of long term position errors and signals for correcting gradiometer bias and drift are determined.

10. The navigation apparatus of claim 9 further comprising:

a gravity sensor for determining gravitation fields;

a vehicle position and velocity sensor,

an integrator, coupled to said vehicle position and velocity sensor and said gravity gradiometer to provide signals representative of an integration over a selected time interval of a product of said gravity gradient and said vehicle velocity, thereby providing output signals representative of a gravity vector having a gravity north component, a gravity east component, and a gravity down component;

a second comparator coupled to said gravity sensor and to said integrator to provide signals representative of differences between measured down component of gravity and reference down component of gravity from which an east velocity error component in said state vector is determined.

11. The navigation apparatus of claim 10 further comprising:

vertical position indicator coupled to said second comparator to provide a vertical position signal;

a height sensor for providing a signal representative of vehicle vertical position;

a geoidal height map memory for providing signals representative of terrain height;

a third comparator coupled to said height sensor and said geoidal height map memory to provide signals representative of vertical difference between measured vertical position obtained from said height sensor and terrain height provide by said geoidal height map memory;

a fourth comparator coupled to said vertical position indicator, said third comparator, and said optimum filter to provide signals, to said optimum filter, that are utilized in said optimum filter to provide a second east velocity error component in said state vector.

12. A method of vehicle navigation comprising the steps of:

providing a gravity gradiometer to measure gravity gradients;

storing geographic gravity data in a gravity reference map memory;

establishing an optimum filter to provide a state vector, the components of which are utilized to update sensors of the navigation system;

utilizing said gravity gradiometer and said gravity reference map memory to establish a gravity gradient matrix and a reference gravity gradient matrix, respectively;

determining differences between respective components of said gravity gradient matrix and said reference gravity gradient matrix, thereby establishing gravity gradient component differences;

coupling said gravity gradient matrix component differences to said filter for utilization in said state vector to determine long term position errors and to provided correction data for gradiometer bias and drift.

13. A method in accordance with claim 12 further comprising the steps of:

utilizing a vehicle position and velocity sensor to determine vehicle position and velocity;

producing a product of gravity gradient and vehicle velocity;

integrating said product over a preselected time interval to provide an integrated product having a gravity north component, a gravity east component, and a gravity down component;

determining gravity down with a gravity sensor;

providing differences between gravity down established by said gravity sensor and gravity down produced by said integrating step;

coupling said gravity down differences to said optimum filter wherein it is utilized to determine east velocity error component for said state vector.

14. The method of claim 13 further including the steps of:

determining vertical position from said differences between gravity down established by said gravity sensor and gravity down produced by said integrating step;

sensing height of said vehicle;

utilizing a geoidal height map memory to provide terrain height;

determining difference between said vehicle height and said terrain height;

establishing a difference between said vertical position and said difference between said vehicle height and said terrain height; and

utilizing said difference between said vertical position and said difference between said vehicle height and said terrain height to establish a second east velocity error component for said state vector.

15. The method of claim 13 further including the steps of:

storing geographic gravity vertical deflection data;

filtering said gravity vertical deflection data and said integrated product in a complementary manner to provide gravity vertical deflection; and

differencing said gravity vertical deflection and said gravity vertical deflection data to provide a gravity vertical deflection difference;

correcting said vertical deflection difference for speed dependency to provide a speed corrected gravity vertical deflection difference; and

coupling said speed corrected gravity vertical deflection to said optimum filter as a velocity error component of said state vector.

16. The method of claim 15 further including the steps of:

coupling a vertical deflection corrector between said complementary filter and said optimum filter and to said vehicle position and velocity sensor; and

utilizing data coupled to vertical deflection corrector in said optimum filter to determine speed corrected residual vertical deflections.

17. The method of claim 13 further including the steps of:

determining reference gravity anomalies from said gravity reference map memory;

utilizing said gravity sensor to provide measured gravity anomalies;

determining differences between said reference gravity anomalies and said measured gravity anomalies;

coupling said differences between said reference gravity anomalies and said measured gravity anomalies to said optimum filter for the determination of position errors.