High performance Inertial Navigation Systems (INSs) provide continuously very accurate attitude, velocity and position information.
However, it is usually very expensive and because the navigation solutions are obtained by integration, they drift at low frequencies. To obtain very accurate outputs at all frequencies, the INS should be updated periodically using external measurements. On the other hand, the GPS measurements, under ideal conditions, are consistent in accuracy throughout a survey mission. However, such conditions do not often exist.

Independent GPS navigation requires at least four satellites with good geometry. The major drawback of GPS is, therefore, the accuracy degradation due to poor satellite geometry, cycle slips, satellite outages, and dynamic lag during maneuvers. This is especially prevalent in urban centres and when encountering highway overpasses/tunnels. Because of their complementary characteristics, INS is often integrated
with GPS. The integration of GPS and INS provides a system that has superior performance in comparison with either a GPS or an INS stand-alone system. For instance, GPS derived positions have approximately white noise characteristics over the whole frequency range. The GPS-derived positions and velocities are therefore excellent external measurements for updating the INS, thus improving its long-term accuracy.

Similarly, the INS can provide precise position and velocity data for GPS signal acquisition and reacquisition after outages. Recently, focus has been made on developing integrated GPS and low-cost inertial measuring units (IMUs) for commercial applications, for example car navigation. However, due to the low quality of the IMU, huge amount of navigation errors can be generated in very short time intervals. Therefore, low-cost IMUs have shown to be usable only for few seconds
without additional aiding. Furthermore due to the large drift rate of the gyros used in low-cost IMUs, self-alignment cannot be applied for some systems. This paper introduces some of the techniques that overcome the limitations of lowcost IMUs, i.e. the in-filed calibration, the velocity matching alignment, and the use of non-holonomic constraints. 

Tests were conducted using the NovAtel Black Diamond System (BDSTM). A new field calibration method was developed and tested successfully. The new calibration method does not require the IMU to be aligned to the local level frame. Furthermore, the bias estimation of the calibration
method is not affected by the reference gravity error. Almost half of the positioning error could be removed with the accelerometer calibration information. The mechanization and navigation Kalman filter were implemented based on the navigation frame to test the velocity matching alignment and non-holonomic constraints. The velocity matching
alignment technique was tested for the IMUs to which stationary alignment technique cannot be applied. All attitude components converged within three minutes with RMS 0.03. Non-holonomic constraints dramatically reduced the horizontal positioning error, within 40 m for 20 minutes operation. Therefore, low cost INSs can be used as a stand-alone positioning system during the GPS outages of over 10 minutes.

methods

Optimizing the estimation procedure in INS/GPS integration for kinematic applications

Accuarcy improvement of low cost INS/GPS for land applications

GPS and low-cost INS sensors are widely used for positioning and attitude determination applications. Low-cost inertial sensors exhibit large errors that can be compensated using position and velocity updates from GPS. Combining both sensors using a Kalman filter provides high-accuracy, real-time navigation. A conventional Kalman filter relies on the correct definition of the measurement and process noise matrices, which are generally defined a priori and remain fixed throughout the processing run. Adaptive Kalman filtering techniques use the residual sequences to adapt the stochastic properties of the filter on line to correspond to the temporal dependence of the errors involved. This paper examines the use of three adaptive filtering techniques. These are artificially scaling the predicted Kalman filter covariance, the Adaptive Kalman Filter and Multiple Model Adaptive Estimation. The algorithms are tested with the GPS and inertial data simulation software. A trajectory taken from a real marine trial is used to test the dynamic alignment of the inertial sensor errors. Results show that on line estimation of the stochastic properties of the inertial system can significantly improve the speed of the dynamic alignment and potentially improve the overall navigation accuracy and integrity.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0373463302002151

Adaptive Kalman Filtering for Low-cost INS/GPS

GPS and low cost INS sensors are widely used for positioning
and attitude determination applications. Low cost inertial sen-
sors exhibit large errors which are compensated using position
and velocity updates from GPS. Combining both sensors using
a Kalman filter provides high accuracy real time navigation.
The Kalman filter relies on the definition of the correct mea-
surement and process noise matrices which are generally de-
fined a priori and remain fixed throughout the processing
run. Adaptive Kalman filtering techniques use the residual
sequences to adapt the stochastic properties of the filter on-
line to correspond to the temporal dependence of the errors
involved. This paper examines the use of three adaptive fil-
tering techniques. These are artificially scaling the predicted
Kalman filter covariance, the Adaptive Kalman Filter and Mul-
tiple Model Adaptive Estimation.
The algorithms are tested with the IESSG's GPS and inertial
data simulation software. A trajectory taken from a real marine
trial is used to test the dynamic alignment of the inertial sensor
errors. Results show that on-line estimation of the stochastic
properties of the inertial system can significantly improve the
speed of the dynamic alignment and potentially improve the
overall navigation accuracy and integrity.

Adaptive Kalman Filtering for Low Cost INS/GPS

The integration of the Global Positioning System (GPS) and Inertial Navigation Systems (INSs) is often used to provide accurate positioning and navigation information. For applications requiring the highest accuracy, the quality of the inertial sensors required is usually assumed to be very high. This dissertation investigates the integration of GPS with a tactical-grade Inertial Measurement Unit (IMU) for centimetre-level navigation in real-time. Different GPS/INS integration strategies are investigated to assess their relative performance in terms of position and velocity accuracy during partial and complete data outages, carrier phase ambiguity resolution after such data outages, and the overall statistical reliability of the system. In terms of statistical reliability, the traditional equations used in dynamic systems are redeveloped in light of some practical considerations, including centralized and decentralized filter architectures, and sequential versus simultaneous measurement updating. Results show that the integrated solution outperforms the GPS-only approach in all areas. The difference between loose and tight integration strategies was most significant for ambiguity resolution and system reliability. The integrated solution is capable of providing decimetre-level accuracy or better for durations of about five or ten seconds when a complete or partial GPS outage is simulated. This level of accuracy, extended over longer time intervals, is shown to reduce the time required to resolve the L1 ambiguities by an average of about 50% or more for data outages as long as 30 seconds when using a tight integration strategy. More importantly, the reliability of the ambiguity resolution process is improved with the integrated

Optimizing the estimation procedure in INS/GPS integration for kinematic applications

Citations

Accuarcy improvement of low cost INS/GPS for land applications

Adaptive Kalman Filtering for Low-cost INS/GPS

Adaptive Kalman Filtering for Low Cost INS/GPS

Real-time integration of a tactical-grade IMU and GPS for high-accuracy positioning and navigation

Improving the Inertial Navigation System (INS) error model for INS and INS/DGPS applications

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