Center for Mapping

Tightly Integrated GPS/INS and Kalman Filtering

 

The AIMS concept is based on the implementation of tight GPS/INS integration, where the Kalman filter optimally estimates position, velocity, and attitude errors, as well as errors in the inertial and GPS measurements. The tight integration, as implemented in AIMS™, is preferred as opposed to the loose coupling approach, as it allows for continuous IMU error calibration and positioning even if the number of satellites drops below four.  Furthermore, the high accuracy of the well-calibrated inertial system over short time periods allows correction of cycle-slips affecting GPS measurements, and supports more robust OTF ambiguity resolution, even after a total loss of GPS lock. Although, the extended losses of GPS lock do not occur very frequently in the airborne mapping scenario, since careful mission planning and avoiding steep banking will usually assure a continuous lock on several satellites, the power of tight integration becomes more important for the low-altitude missions (300-500 m), especially over urban areas. At this level, the radio interference (possibly from commercial VHF radio, over-the-horizon military radar, broadcast television, mobile satellite system telephones, etc.) causes signal reception problems, resulting in a very low signal-to-noise ratio, leading ultimately to extended losses of GPS lock

Implementation of closed-loop INS error calibration allows continuous, OTF error update that bounds INS errors, leading to increased estimation accuracy. The inertial strapdown algorithm is implemented for processing raw IMU data, providing the navigation solution. The attitude determination algorithm adapted in our strapdown solution uses quaternions, whose numerical stability is superior to other methods (i.e., Euler angles and direct cosine method). A single Kalman filter, with number of states equal to 21 plus the number of double differences, is then used to process the GPS double-differenced phases, combined with the inertial solution. The state unknowns are errors in position, velocity, and orientation, three biases and three scale factors for the accelerometers, three gyro drifts, two deflections of the vertical and the gravity anomaly. In addition, GPS ionospheric delay is estimated for every satellite in the solution (Table 1).

           

 

Kalman Filter States	Number of States
Navigation Parameters ·        Position errors ·        Velocity errors ·        Attitude errors ·        Heading errors	3 3 2 1
Accelerometer Errors (random walk) ·        Biases ·        Scale factor errors	3 3
Gyro Errors (random walk) ·        Drifts	3
Gravity ·        Deflection (Gauss-Markov, 20nmi) ·         Anomaly  (Gauss-Markov, 20nmi)	2 1
GPS Errors ·        Ionospheric delay (random walk)	Number of DD

 

Table 1. System parameters.

 

AIMS filter implementation is explained in more details in the paper “Gravity Modeling for High-Accuracy GPS/INS Integration”.