Real-time integrated vehicle positioning method and system with differential GPS

• US 6,496,778 B1
• Filed: 09/14/2000
• Issued: 12/17/2002
• Est. Priority Date: 09/14/2000
• Status: Expired due to Term

• Current Assignee: American GNC Corporation
• Original Assignee: American GNC Corporation
• Inventors: Lin, Ching-Fang
• Primary Examiner(s): Zanelli, Michael J.
• Assistant Examiner(s): GIBSON, ERIC M
• Application Number:
  US09/661,587
• Time in Patent Office: 824 Days
• Field of Search: 701/214, 701/215, 701/216, 701/221, 701/220, 342/357.03, 342/357.04, 342/357.05, 342/357.06, 342/357.14
• US Class Current: 701/215
• CPC Class Codes: G01S 19/26 involving a sensor measurement for aiding acquisition or tracking
• CPC Class Codes: G01S 19/41 Differential correction, e.g. DGPS [differential GPS]
• CPC Class Codes: G01S 19/47 the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
• CPC Class Codes: G01S 19/55 Carrier phase ambiguity resolution; Floating ambiguity; LAMBDA [Least-squares AMBiguity Decorrelation Adjustment] method

Abstract

A real-time integrated vehicle positioning method and system with differential GPS can substantially solve the problems encountered in either the global positioning system-only or the inertial navigation system-only, such as loss of global positioning satellite signal, sensitivity to jamming and spoofing, and an inertial solution'"'"'s drift over time. In the present invention, the velocity and acceleration from an inertial navigation processor of the integrated GPS/INS system are used to aid the code and carrier phase tracking of the global positioning system satellite signals, so as to enhance the performance of the global positioning and inertial integration system, even in heavy jamming and high dynamic environments. To improve the accuracy of the integrated GPS/INS navigation system, phase measurements are used and the idea of the differential GPS is employed. However, integer ambiguities have to be resolved for high accuracy positioning. Therefore, in the present invention a new on-the-fly ambiguity resolution technique is disclosed to resolve double difference integer ambiguities. The real-time fully-coupled GPS/IMU vehicle positioning system includes an IMU (inertial measurement unit), a GPS processor, and a data link which are connected to a central navigation processor to produce a navigation solution that is output to an I/O (input/output) interface.

Claims

1. A real-time integrated vehicle positioning system with differential GPS, comprising:

a global positioning system (GPS) processor for providing GPS rover measurements including pseudorange, carrier phase, and Doppler shift;

a data link for receiving GPS measurements including pseudorange, carrier phase, Doppler shift, position, and velocity from a reference site;

an inertial measurement unit (IMU) for providing inertial measurements including body angular rates and specific forces;

a central navigation processor, which is connected with said GPS processor, said IMU and said data link, comprising an inertial navigation system (INS) processor, a Kalman filter, a new satellites/cycle slips detection module, and an on-the-fly ambiguity resolution module; and

an input/output (I/O) interface connected to said central navigation processor;

wherein said GPS measurements from said GPS processor and said data link are passed to said central navigation processor and said inertial measurements are injected into said inertial navigation system (INS) processor;

wherein an output of said INS processor and said GPS measurements are blended in said Kalman filter;

an output of said Kalman filter is fed back to said INS processor to correct an INS navigation solution, which is then output from said central navigation processor to said I/O interface;

wherein said INS processor provides velocity and acceleration data injected into said GPS processor to aid code and carrier phase tracking of GPS satellite signals;

wherein an output of said GPS processor, an output of said data link, and an output of said INS processor are injected into a new satellites/cycle slips detection module to test the occurrence of new satellites and cycle slips, wherein as said new satellites/cycle slips detection module is on, said on-the-fly ambiguity resolution module is activated to resolve global positioning system satellite signal carrier phase integer ambiguities;

wherein said on-the-fly ambiguity resolution module outputs the integer ambiguities into said Kalman filter to further improve positioning accuracy, and said INS processor outputs navigation data to said I/O interface.

2. The real-time integrated vehicle positioning system, as recited in claim 1, wherein said GPS processor outputs pseudorange, Doppler shifts, global positioning system satellite ephemeris, and atmosphere parameters to said Kalman filter.

3. The real-time integrated vehicle positioning system, as recited in claim 2, wherein said INS processor processes said inertial measurements, which are body angular rates and specific forces, and a position error, a velocity error, and an attitude error coming from said Kalman filter to derive said corrected navigation solution.

4. The real-time integrated vehicle positioning system, as recited in claim 3, wherein said INS processor comprises an IMU I/O interface, an IMU error compensation module, a coordinate transformation computation module, an attitude position velocity computation module, a transformation matrix computation module, and an earth and vehicle rate computation module,

wherein said IMU I/O interface collects signals of said body angular rates and specific forces from said IMU for processing and converting to digital data which are corrupted by inertial sensor measurement errors to form contaminated data that are passed to said IMU error compensation module,

wherein said IMU error compensation module receives sensor error estimates derived from said Kalman filter to perform IMU error mitigation on said IMU data,

wherein said corrected inertial data are sent to said coordinate transformation computation module and said transformation matrix computation module, where said body angular rates are sent to said transformation matrix computation module and said specific forces are sent to said coordinate transformation computation module,

wherein said transformation matrix computation module receives said body angular rates from said IMU error computation module and an earth and vehicle rate from said earth and vehicle rate computation module to perform transformation matrix computation,

wherein said transformation matrix computation module sends said transformation matrix to said coordinate transformation computation module and said attitude position velocity computation module,

wherein said coordinate transformation module collects said specific forces from said IMU error computation module and said transformation matrix from said transformation matrix computation module to perform said coordinate transformation,

wherein said coordinate transformation computation sends said specific forces transferred into said coordinate system presented by said transformation matrix to said attitude position velocity computation module, wherein said attitude position velocity computation module receives said transformed specific forces from said coordinate transformation computation module and said transformation matrix from said transformation matrix computation module to perform an attitude, position, velocity update.

5. The real-time integrated vehicle positioning system, as recited in claim 4, wherein after computation of said position and velocity, position and velocity errors which are calculated by said Kalman filter are used in said attitude position velocity computation module to correct an inertial solution.

6. The real-time integrated vehicle positioning system, as recited in claim 5, wherein said attitude error computed by said Kalman filter is sent to said attitude position velocity computation module to perform an attitude correction in said attitude position velocity computation module.

7. The real-time integrated vehicle positioning system, as recited in claim 5, wherein said attitude error computed by said Kalman filter is sent to said attitude position velocity computation module to perform an attitude correction in said attitude position velocity computation module.

8. The real-time integrated vehicle positioning system, as recited in claim 5, wherein the corrected inertial solution, which is obtained from said attitude position velocity computation module, is passed to said Kalman filter to construct said measurements, moreover a corrected inertial navigation solution is also sent to said new satellites/cycle slips detection module, and said on-the-fly ambiguity resolution module to aid said global positioning system satellite carrier phase integer ambiguity fixing, and that corrected velocity and acceleration are passed to said GPS processor to aid said global positioning system satellite signal carrier phase and code tracking, wherein attitude, position, and velocity computed by said attitude position velocity computation module are sent to said earth and vehicle rate computation module to calculate an Earth rotation rate and a vehicle rotation rate which are sent to said transformation matrix computation module, wherein said attitude, position, and velocity information are sent to said I/O interface which provides a navigation data source for avionics systems onboard a vehicle.

9. The real-time integrated vehicle positioning system, as recited in claim 6, wherein the corrected inertial solution, which is obtained from said attitude position velocity computation module, is passed to said Kalman filter to construct said measurements, moreover a corrected inertial navigation solution is also sent to said new satellites/cycle slips detection module, and said on-the-fly ambiguity resolution module to aid said global positioning system satellite carrier phase integer ambiguity fixing, and that corrected velocity and acceleration are passed to said GPS processor to aid said global positioning system satellite signal carrier phase and code tracking, wherein attitude, position, and velocity computed by said attitude position velocity computation module are sent to said earth and vehicle rate computation module to calculate an Earth rotation rate and a vehicle rotation rate which are sent to said transformation matrix computation module, wherein said attitude, position, and velocity information are sent to said I/O interface which provides a navigation data source for avionics systems onboard a vehicle.

10. The real-time integrated vehicle positioning system, as recited in claim 7, wherein the corrected inertial solution obtained from said attitude position velocity computation module is passed to said Kalman filter to construct said measurements, moreover the corrected inertial navigation solution is also sent to said new satellites/cycle slips detection module, and said on-the-fly ambiguity resolution module to aid said global positioning system satellite carrier phase integer ambiguity fixing, and that the corrected velocity and acceleration is passed to microprocessor of said GPS processor to aid said global positioning system satellite signal carrier phase and code tracking, wherein attitude, position, and velocity computed by said attitude position velocity computation module are sent to said earth and vehicle rate computation module to calculate an Earth rotation rate and a vehicle rotation rate which are sent to said transformation matrix computation module, wherein said attitude, position, and velocity information are sent to said I/O interface which provides a navigation data source for avionics systems onboard a vehicle.

11. The real-time integrated vehicle positioning system, as recited in one of claims 1 to 10, wherein said Kalman filter is a robust Kalman filter for providing near-optimal performance over a large class of process and measurement models and for blending GPS measurements and inertial sensor measurements.

12. The real-time integrated vehicle positioning system, as recited in claim 11, wherein said robust Kalman filter comprises a GPS error compensation module for gathering said pseudorange, said carrier phase, and said Doppler shift of said GPS measurements from said GPS processor and said data link, and position and velocity corrections from an updating state vector module to perform GPS error compensation to form corrected GPS raw data, including a pseudorange, a carrier phase, and a Doppler frequency, which are sent to a preprocessing module,

wherein said preprocessing module receives GPS satellite ephemeris from said GPS processor said corrected GPS raw data from said GPS error compensation module, and INS solutions from said INS processor, said preprocessing module performing calculation of state transit matrix and sending with a state vector to a state vector prediction module,

wherein said calculated state transit matrix is also sent to a covariance propagation module which calculates a measurement matrix and a current measurement vector according to a computed measurement matrix and a measurement model, and that said measurement matrix and said computed current measurement vector are passed to a computing measurement residue module, said state vector prediction module receiving said state transit matrix and said state vector from said preprocessing module to perform state prediction of current epoch, said predicted current state vector being passed to said computing measurement residue module which receives predicted current state vector from said state vector prediction module and said measurement matrix and said current measurement vector from said preprocessing module,

wherein said computing measurement residue module calculates measurement residues by subtracting multiplication of said measurement matrix and said predicted current state vector from said current measurement vector, and said measurement residues are sent to a residue monitor module and said updating state vector module,

wherein said residue monitor module performs a discrimination on said measurement residues received from said computing measurement residue module,

wherein said covariance propagation module gathers covariance of system process from said residue monitor module, said state transit matrix from said preprocessing module, and covariance of estimated error to calculate current covariance of said estimated error which is sent to a computing optimal gain module,

wherein said computing optimal gain module receives said current covariance of said estimated error from said covariance computing module to compute optimal gain which is passed to a covariance updating module and said updating state vector module, said covariance updating module updating said covariance of said estimated error and sending to said covariance propagation module,

wherein said updating state vector module receives said optimal gain from said computing optimal gain module and said measurement residues from said computing measurement residue module, said updating state vector calculating a current estimate of state vector including position, velocity and attitude errors and sending to said GPS error compensation module and said INS processor.

13. The real-time integrated vehicle positioning system, as recited in one of claims 1 to 10, wherein said on-the-fly ambiguity resolution module collects position and velocity data from said INS processor, said carrier phase and Doppler shift measurement from said GPS processor and said datalink, and covariance matrix from said Kalman filter to fix said global positioning system satellite signal integer ambiguity number, wherein after fixing of carrier phase ambiguities, said carrier phase ambiguity number is passed to said Kalman filter to further improve a measurement accuracy of a global positioning system raw data.

14. A real-time integrated vehicle positioning method with differential GPS, comprising the steps of:

(a) receiving global positioning system (GPS) raw measurements of a vehicle, including pseudorange, carrier phase, and Doppler shift;

at the same time, receiving the raw measurements, position, and velocity from a reference site through a data link;

(b) sending said GPS raw measurements to a central navigation processor from a GPS processor and said data link;

(c) receiving a plurality of inertial measurements of said vehicle including body angular rates and specific forces from an inertial measurement unit (IMU);

(d) sending said inertial measurements from said IMU to an inertial navigation system (INS) processor of said central navigation processor for computing an inertial navigation solution which includes position, velocity, acceleration, and attitude of said vehicle;

(e) fixing integer ambiguities by testing an occurrence of new satellites or cycle slips using said GPS raw measurements from said GPS processor, OPS reference raw measurements, position, and velocity from said data link, and said inertial navigation solution from said INS processor, and sending said integer ambiguities to a Kalman filter;

(f) blending said inertial navigation solution derived from said INS processor and said GPS raw measurements from said GPS processor and said data link in said Kalman filter to derive INS corrections and GPS corrections;

(g) feeding back said INS corrections from said Kalman filter to said INS processor to correct said inertial navigation solution; and

(h) sending said inertial navigation solution from said INS processor to an I/O interface so as to provide navigation data for an on-board avionics system.

15. The real-time integrated vehicle positioning method, as recited in claim 14, after the step (h), further comprising an additional step of injecting said velocity and acceleration of said vehicle from said INS processor into said GPS processor to aid a plurality of global positioning system code tracking loops and a plurality of global positioning system carrier phase tracking loops for requiring and tracking said global positioning system satellite signals, wherein said GPS processor outputs said GPS raw measurements including said pseudorange, said carrier phase, and said Doppler shift.

16. The real-time integrated vehicle positioning method, as recited in claim 14, wherein the step (e) further comprises the steps of:

(e.1) injecting said GPS raw measurements from said GPS processor, GPS reference raw measurements, position, and velocity from said data link, and said inertial navigation solution from said INS processor into a new satellites/cycle slips detection module to test said occurrence of new satellites or cycle slips;

(e.2) initiating an on-the-fly ambiguity resolution module as said new satellites/cycle slips detection module is on when said new satellites or cycle slips occur;

(e.3) fixing integer ambiguities to estimate an accurate vehicle navigation solution, and

(e.4) sending said integer ambiguities from said on-the-fly ambiguity resolution module to said Kalman filter.

17. A real-time integrated vehicle positioning method with differential GPS, as recited in claim 15, wherein the step (e) further comprises the steps of:

(e.1) injecting said GPS raw measurements from said GPS processor, GPS reference raw measurements, position, and velocity from said data link, and said inertial navigation solution from said INS processor into a new satellites/cycle slips detection module to test said occurrence of new satellites or cycle slips;

(e.2) initiating an on-the-fly ambiguity resolution module as said new satellites/cycle slips detection module is on when said new satellites or cycle slips occur;

(e.3) fixing integer ambiguities to estimate an accurate vehicle navigation solution, and

(e.4) sending said integer ambiguities from said on-the-fly ambiguity resolution module to said Kalman filter.

18. The real-time integrated vehicle positioning method, as recited in claim 16, wherein the step (e.2) further comprises the steps of:

(e.2.1) setting up a search window which comprises a plurality of time (N) epochs;

(e.2.2) searching an integer ambiguity set at a first time epoch of said search window by using an intermediate ambiguity search strategy (IASS), wherein an integer ambiguity set becomes a member of an estimator bank while there is no member in said estimator bank before a first time epoch, wherein based on said integer ambiguity set and phase measurements, a rover position is estimated in said estimator bank, and then a corresponding weight is calculated in a weight bank, as a result, an optimal rover position for said first time epoch is equal to said rover position multiplied by said corresponding weight, and based on an optimal rover position and said Doppler shifts, a rover velocity is estimated;

(e.2.3) searching said integer ambiguity set at a second time epoch of said search window by using said IASS;

(e.2.4) following the step (e.2.3) for other time epochs of said search window, wherein at a last time epoch N of said search window, after said search of said IASS, said estimator bank and said weight bank are completely established;

(e.2.5) inputting continuously said phase measurements into said Kalman filter of said estimator bank at the (N+1)^th time epochs, wherein based on said integer ambiguity set and said phase measurements, said rover position is estimated in said estimator bank and said corresponding weight is calculated accumulatively in said weight bank to an associated weight, wherein said optimal rover position is equal to a sum of said rover position multiplied by said associated weight, and further based on said optimal rover position and Doppler shifts, said rover velocity is estimated;

(e.2.6) following the step (e.2.5) after said (N+1)^th time epoch until a criterion is met, wherein after said criterion is met, said estimator bank and said weight bank stop functioning, and during a confirmation period, that is from said first time epoch of said search window to said last time epoch when said estimator bank and said weight bank stop functioning, said estimator bank and said weight bank continuously identify a correct integer ambiguity set and estimate said rover position in real-time, wherein a weight corresponding to said correct integer ambiguity is approaching to one while said integer ambiguity set is converging to zero; and

(e.2.7) estimating said rover position and velocity by using a least-squares estimated method after said fixing integer ambiguities;

as said new satellites or cycle slips occur, thereby the steps (e.2.1) to (e.2.7) are initiated.

19. The real-time integrated vehicle positioning method, as recited in claim 17, wherein the step (e.2) further comprises the steps of:

(e.2.1) setting up a search window which comprises a plurality of time (N) epochs;

(e.2.2) searching an integer ambiguity set at a first time epoch of said search window by using an intermediate ambiguity search strategy (IASS), wherein an integer ambiguity set becomes a member of an estimator bank while there is no member in said estimator bank before a first time epoch, wherein based on said integer ambiguity set and phase measurements, a rover position is estimated in said estimator bank, and then a corresponding weight is calculated in a weight bank, as a result, an optimal rover position for said first time epoch is equal to said rover position multiplied by said corresponding weight, and based on an optimal rover position and said Doppler shifts, a said rover velocity is estimated;

(e.2.3) searching said integer ambiguity set at a second time epoch of said search window by using said IASS;

(e.2.4) following the step (e.2.3) for other time epochs of said search window, wherein at a last time epoch N of said search window, after said search of said IASS, said estimator bank and said weight bank are completely established;

(e.2.5) inputting continuously said phase measurements into said Kalman filter of said estimator bank at the (N+1)^th time epochs, wherein based on said integer ambiguity set and said phase measurements, said rover position is estimated in said estimator bank and said corresponding weight is calculated accumulatively in said weight bank to an associated weight, wherein said optimal rover position is equal to a sum of said rover position multiplied by said associated weight, and further based on said optimal rover position and Doppler shifts, said rover velocity is estimated;

(e.2.6) following the step (e.2.5) after said (N+1)^th time epoch until a criterion is met, wherein after said criterion is met, said estimator bank and said weight bank stop functioning, and during a confirmation period, that is from said first time epoch of said search window to said last time epoch when said estimator bank and said weight bank stop functioning, said estimator bank and said weight bank continuously identify a correct integer ambiguity set and estimate said rover position in real-time, wherein a weight corresponding to said correct integer ambiguity is approaching to one while said integer ambiguity set is converging to zero; and

(e.2.7) estimating said rover position and velocity by using a least-squares estimated method after said fixing integer ambiguities;

as said new satellites or cycle slips occur, thereby the steps (e.2.1) to (e.2.7) are initiated.

20. The real-time integrated vehicle positioning method, as recited in claim 18, in the step (e.2.3), wherein when said integer ambiguity set is same as one of said time epochs, a number of said Kalman filter remains, wherein based on said integer ambiguity set and said phase measurements, said rover position is estimated in said estimator bank and said corresponding weight is accumulatively calculated in said weight bank, as a result, said optimal rover position is equal to said rover position multiplied by said associated weight and based on said optimal rover position and said Doppler shifts, said rover velocity is estimated.

21. The real-time integrated vehicle positioning method, as recited in claim 18, in the step (e.2.3), wherein when said integer ambiguity set is different from one of said time epoch, a current integer ambiguity set becomes a new member of said estimator bank, that is a number of said Kalman filter increases by one, wherein based on said integer ambiguity set and said phase measurements, said rover position is estimated in said estimator bank and a calculation of each corresponding weight is recalculated from scratch in said weight bank, and therefore said optimal rover position is equal to a sum of said rover position multiplied by said associated weight, wherein based on said optimal rover position and said Doppler shifts, said rover velocity is estimated.

22. The real-time integrated vehicle positioning method, as recited in claim 19, in the step (e.2.3), in the step (e.2.3), wherein when said integer ambiguity set is same as one of said time epochs, a number of said Kalman filter remains, wherein based on said integer ambiguity set and said phase measurements, said rover position is estimated in said estimator bank and said corresponding weight is accumulatively calculated in said weight bank, as a result, said optimal rover position is equal to said rover position multiplied by said associated weight and based on said optimal rover position and said Doppler shifts, said rover velocity is estimated.

23. The real-time integrated vehicle positioning method, as recited in claim 19, in the step (e.2.3), in the step (e.2.3), wherein when said integer ambiguity set is different from one of said time epoch, a current integer ambiguity set becomes a new member of said estimator bank, that is a number of said Kalman filter increases by one, wherein based on said integer ambiguity set and said phase measurements, said rover position is estimated in said estimator bank and a calculation of each corresponding weight is recalculated from scratch in said weight bank, and therefore said optimal rover position is equal to a sum of said rover position multiplied by said associated weight, wherein based on said optimal rover position and said Doppler shifts, said rover velocity is estimated.

24. The real-time integrated vehicle positioning method and system with differential GPS, as recited in claim 18, wherein said IASS comprises the steps of:

resolving primary double difference wide lane ambiguities in a primary double difference wide lane ambiguity resolution module, wherein a priori information of said rover position obtained from ionosphere-free pseudorange measurements and an approximated double difference wide lane ambiguities are combined with a primary double difference wide lane phase measurements to estimate said rover position and said primary double difference wide lane ambiguities;

establishing an ambiguity search domain based on estimated primary double difference wide lane ambiguities and a corresponding cofactor matrix;

searching for an ambiguity set by using a “

simplified”

least-squares search estimator;

computing said rover position based on said primary double difference wide lane ambiguities in a position calculation module;

fixing secondary double difference wide lane ambiguities by applying a primary wide-lane-ambiguity-fixed rover position solution into secondary double difference wide lane phase measurements;

calculating approximated double difference narrow lane ambiguities and then using an extrawidelaning technique module to resolve double difference narrow lane ambiguities; and

calculating L1 and L2 ambiguities in a L1 and L2 ambiguity resolution module from a combination of said primary and secondary double difference wide lane ambiguities and said double difference narrow lane ambiguities.

25. The real-time integrated vehicle positioning method and system with differential GPS, as recited in claim 19, wherein said IASS comprises the steps of:

resolving primary double difference wide lane ambiguities in a primary double difference wide lane ambiguity resolution module, wherein a priori information of said rover position obtained from ionosphere-free pseudorange measurements and an approximated double difference wide lane ambiguities are combined with a primary double difference wide lane phase measurements to estimate said rover position and said primary double difference wide lane ambiguities;

establishing an ambiguity search domain based on estimated primary double difference wide lane ambiguities and a corresponding cofactor matrix;

searching for an ambiguity set by using a “

simplified”

least-squares search estimator;

computing said rover position based on said primary double difference wide lane ambiguities in a position calculation module;

fixing secondary double difference wide lane ambiguities by applying a primary wide-lane-ambiguity-fixed rover position solution into secondary double difference wide lane phase measurements;

calculating approximated double difference narrow lane ambiguities and then using an extrawidelaning technique module to resolve double difference narrow lane ambiguities; and

calculating L1 and L2 ambiguities in a L1 and L2 ambiguity resolution module from a combination of said primary and secondary double difference wide lane ambiguities and said double difference narrow lane ambiguities.

26. The real-time integrated vehicle positioning method and system with differential GPS, as recited in claim 20, wherein said IASS comprises the steps of:

resolving primary double difference wide lane ambiguities in a primary double difference wide lane ambiguity resolution module, wherein a priori information of said rover position obtained from ionosphere-free pseudorange measurements and an approximated double difference wide lane ambiguities are combined with a primary double difference wide lane phase measurements to estimate said rover position and said primary double difference wide lane ambiguities;

establishing an ambiguity search domain based on estimated primary double difference wide lane ambiguities and a corresponding cofactor matrix;

searching for an ambiguity set by using a “

simplified”

least-squares search estimator;

computing said rover position based on said primary double difference wide lane ambiguities in a position calculation module;

fixing secondary double difference wide lane ambiguities by applying a primary wide-lane-ambiguity-fixed rover position solution into secondary double difference wide lane phase measurements;

calculating approximated double difference narrow lane ambiguities and then using an extrawidelaning technique module to resolve double difference narrow lane ambiguities; and

calculating L1 and L2 ambiguities in a L1 and L2 ambiguity resolution module from a combination of said primary and secondary double difference wide lane ambiguities and said double difference narrow lane ambiguities.

27. The real-time integrated vehicle positioning method and system with differential GPS, as recited in claim 21, wherein said IASS comprises the steps of:

resolving primary double difference wide lane ambiguities in a primary double difference wide lane ambiguity resolution module, wherein a priori information of said rover position obtained from ionosphere-free pseudorange measurements and an approximated double difference wide lane ambiguities are combined with a primary double difference wide lane phase measurements to estimate said rover position and said primary double difference wide lane ambiguities;

establishing an ambiguity search domain based on estimated primary double difference wide lane ambiguities and a corresponding cofactor matrix;

searching for an ambiguity set by using a “

simplified”

least-squares search estimator;

computing said rover position based on said primary double difference wide lane ambiguities in a position calculation module;

fixing secondary double difference wide lane ambiguities by applying a primary wide-lane-ambiguity-fixed rover position solution into secondary double difference wide lane phase measurements;

calculating approximated double difference narrow lane ambiguities and then using an extrawidelaning technique module to resolve double difference narrow lane ambiguities; and

calculating L1 and L2 ambiguities in a L1 and L2 ambiguity resolution module from a combination of said primary and secondary double difference wide lane ambiguities and said double difference narrow lane ambiguities.

28. The real-time integrated vehicle positioning method and system with differential GPS, as recited in claim 22, wherein said IASS comprises the steps of:

resolving primary double difference wide lane ambiguities in a primary double difference wide lane ambiguity resolution module, wherein a priori information of said rover position obtained from ionosphere-free pseudorange measurements and an approximated double difference wide lane ambiguities are combined with a primary double difference wide lane phase measurements to estimate said rover position and said primary double difference wide lane ambiguities;

establishing an ambiguity search domain based on estimated primary double difference wide lane ambiguities and a corresponding cofactor matrix;

searching for an ambiguity set by using a “

simplified”

least-squares search estimator;

computing said rover position based on said primary double difference wide lane ambiguities in a position calculation module;

fixing secondary double difference wide lane ambiguities by applying a primary wide-lane-ambiguity-fixed rover position solution into secondary double difference wide lane phase measurements;

calculating approximated double difference narrow lane ambiguities and then using an extrawidelaning technique module to resolve double difference narrow lane ambiguities; and

calculating L1 and L2 ambiguities in a L1 and L2 ambiguity resolution module from a combination of said primary and secondary double difference wide lane ambiguities and said double difference narrow lane ambiguities.

29. The real-time integrated vehicle positioning method and system with differential GPS, as recited in claim 23, wherein said IASS comprises the steps of:

resolving primary double difference wide lane ambiguities in a primary double difference wide lane ambiguity resolution module, wherein a priori information of said rover position obtained from ionosphere-free pseudorange measurements and an approximated double difference wide lane ambiguities are combined with a primary double difference wide lane phase measurements to estimate said rover position and said primary double difference wide lane ambiguities;

establishing an ambiguity search domain based on estimated primary double difference wide lane ambiguities and a corresponding cofactor matrix;

searching for an ambiguity set by using a “

simplified”

least-squares search estimator;

computing said rover position based on said primary double difference wide lane ambiguities in a position calculation module;

fixing secondary double difference wide lane ambiguities by applying a primary wide-lane-ambiguity-fixed rover position solution into secondary double difference wide lane phase measurements;

calculating approximated double difference narrow lane ambiguities and then using an extrawidelaning technique module to resolve double difference narrow lane ambiguities; and

calculating L1 and L2 ambiguities in a L1 and L2 ambiguity resolution module from a combination of said primary and secondary double difference wide lane ambiguities and said double difference narrow lane ambiguities.

30. The real-time integrated vehicle positioning method, as recited in claim 24, wherein said a priori information about said rover position is given by said outputs of said INS processor.

31. The real-time integrated vehicle positioning method, as recited in claim 25, wherein said a priori information about said rover position is given by said outputs of said INS processor.

32. The real-time integrated vehicle positioning method, as recited in claim 26, wherein said a priori information about said rover position is given by said outputs of said INS processor.

33. The real-time integrated vehicle positioning method, as recited in claim 27, wherein said a priori information about said rover position is given by said outputs of said INS processor.

34. The real-time integrated vehicle positioning method, as recited in claim 28, wherein said a priori information about said rover position is given by said outputs of said INS processor.

35. The real-time integrated vehicle positioning method, as recited in claim 29, wherein said a priori information about said rover position is given by said outputs of said INS processor.

36. The real-time integrated vehicle positioning method, as recited in claim 16, wherein when no new satellites and cycle slips occur, in the step (e), that is said new satellites/cycle slips detection module is off, the steps (e.2) to (e.4) are bypassed, that is an on-the-fly ambiguity resolution module is off, too, and said integer ambiguities are already resolved in said Kalman filter.

37. The real-time integrated vehicle positioning method, as recited in claim 17, wherein when no new satellites and cycle slips occur, in the step (e), that is said new satellites/cycle slips detection module is off, the steps (e.2) to (e.4) are bypassed, that is an on-the-fly ambiguity resolution module is off, too, and said integer ambiguities are already resolved in said Kalman filter.