Global Positioning Systems, Inertial Navigation, and Integration

Chapter 10: GNSS/INS INTEGRATION

10.1 BACKGROUND

10.1.1 Sensor Integration

GNSS/INS integration is a form of sensor integration or sensor fusion, which involves combining the outputs of different sensor systems to obtain a better estimate of what they are sensing.

A GNSS receiver is a position sensor. It may use velocity estimates to reduce filter lags, but its primary output is the position of its antenna relative to an earthcentered coordinate system. GNSS position errors will depend on the availability and geometric distribution of GNSS satellites it can track, and other error sources described in Chapter 5. The resulting RMS position errors will be bounded, except for those times when there are not enough satellite signals available for a position solution.

An INS uses acceleration and attitude (or attitude rate) sensors, but its primary output as a sensor system is also position the position of its ISA relative to an earth-centered coordinate system. INS position errors depend on the quality of its inertial sensors and earth models, described in Chapter 9. Although their short-term position errors are very smooth, RMS position errors are not bounded. They do tend to grow over time, and without bound.

This chapter is about practical methods for combining GNSS and INS outputs to improve overall system performance metrics, including

  • RMS position estimation error under nominal GNSS conditions, when the receiver can track enough satellites to obtain a good position estimate.
  • RMS position estimation error when the GNSS receiver cannot track enough satellites to obtain a good position estimate. This can happen as a result of
    • Background noise or signal jamming,
    • Blocking by the leaf canopy, buildings, tunnels, etc.
    • GNSS system failures
    • Vehicle attitudes pointing the GNSS antenna pattern downward, or
    • Violent vehicle dynamics causing loss of signal phase lock.
  • RMS velocity estimation error, which is important for
    • Aircraft autonomous landing systems (ALS),
    • Attitude transfer alignment to auxiliary INS
    • Guided weapon delivery
    • Rendezvous and docking maneuvers
  • RMS attitude estimation error, which is important for
    • Sensor pointing
    • Host vehicle guidance and control
  • Maintaining GNSS satellite signal lock, which can be difficult under severe dynamic conditions. In effect, INS accelerometers measure the derivative of signal Doppler shift, which can be used to improve GNSS receiver phaselock control margins.

Evaluating these metrics for a proposed system application requires statistical models for how the component error sources contribute to overall system performance under conditions of the intended mission trajectories.

The purpose of this chapter is to present these models, and the techniques for applying them to evaluate the expected integrated performance for a specific INS design, GNSS configuration and ensemble of mission trajectories.

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