Scheduled Maintenance We will be conducting scheduled maintenance on 17th of May from 10:00 PM EST to 18th of May​​ 9:00 PM EST. During this time MyAerospace applications will be temporarily unavailable. We apologize for any inconvenience this may cause.
Scheduled Maintenance Attention : Several of our website applications will be unavailable from Friday 05/03 08:30 PM EST through Sunday 05/05 08:30 AM EST due to Scheduled Maintenance as we work to improve performance. Thank you for your patience.
×

Your browser is not supported.

For the best experience, please access this site using the latest version of the following browsers:

Close This Window

By closing this window you acknowledge that your experience on this website may be degraded.

Boosting Navigation Confidence: The Power of Velocity Aiding for INS/GNSS Navigators

Boosting Navigation Confidence: The Power of Velocity Aiding for INS/GNSS Navigators

Inertial Navigation Systems (INS) and Global Navigation Satellite Systems (GNSS) are commonly used for navigation in various applications, including aerospace, marine, and ground vehicles. While GNSS provides accurate positioning and velocity information, it has limitations, such as signal blockage, multipath, and interference. On the other hand, an INS can provide continuous navigation solutions, but its accuracy degrades over time due to errors caused by the inertial sensors. Effectively blending data from these sensors improves the overall navigation solutions, but in extended GNSS denied environments the inertial drift is still a problem, especially where a lower performing IMU is used due to cost, size, weight and export classification restrictions.

To overcome these limitations, and improve GNSS-denied performance, Velocity Aiding (VA) is often used. Let’s explore the concept of VA and its usefulness for INS/GNSS navigators, especially when GNSS signals aren’t available.

Velocity Aiding is a technique that uses the velocity measurements from a secondary source to improve the accuracy of the INS solution. The secondary source can be any device that measures vehicle velocity, such as Doppler radar or laser rangefinder for airborne platforms, Doppler Velocity Log (DVL) for marine applications and various wheel encoder technologies for land-based applications. Each technology has its own pros and cons regarding suitability and accuracy for the application in mind. For example, a wheel encoder is typically very accurate, but can introduce errors if the wheels are slipping/spinning against the ground if the sensor fusion software isn’t well written.

The velocity measurements from the VA sensor are integrated into the INS solution to correct for the drift caused by the inertial sensors. This technique is particularly useful in scenarios where GNSS signals are blocked or unavailable, such as in urban canyons, tunnels, and indoor environments. VA can improve the accuracy of the INS solution over time. INS solutions suffer from drift caused by the integration of the inertial sensors, which leads to errors in position and velocity estimates. By integrating velocity measurements from a secondary source, the sensor fusion software can correct for these errors and provide a more accurate solution over time. This is particularly useful in applications where long-term accuracy is important, such as in autonomous vehicles or aircraft. For example, the Honeywell HGuide n580 with velocity data from a wheel encoder will only drift by <0.05% of the distance travelled when GNSS is unavailable.

VA can also improve the reliability of the INS/GNSS solution. In scenarios where GNSS signals are weak or unavailable, the INS solution may suffer from accumulated errors and drift. This can lead to incorrect position and velocity estimates, which can be dangerous in critical applications. By using VA, the INS solution can be continuously updated with velocity information from a secondary source, providing a more reliable solution even in challenging environments.

There are several techniques for implementing VA in INS/GNSS systems. One approach is to use a Kalman filter to fuse the velocity measurements from the secondary source with the INS solution. The Kalman filter provides an optimal estimate of the state of the system by combining the measurements from multiple sources. Another approach is to use a loosely coupled integration scheme, where the velocity measurements from the secondary source are used to update the velocity state of the INS while keeping the position estimates independent of the secondary source. This approach allows for more flexibility in the integration process and can be beneficial when the accuracy of the secondary source is lower than the INS.

One important consideration when using VA is the alignment of the coordinate systems between the secondary source and the INS. The velocity measurements from the secondary source need to be properly transformed and aligned with the coordinate system of the INS to ensure accurate integration. This requires a calibration process and careful synchronization of the measurements.

While VA can significantly improve the accuracy and reliability of INS/GNSS navigators in the absence of GNSS signals, it is important to note that it is not a standalone solution. VA should be seen as a complementary technique that enhances the performance of the INS by leveraging external velocity measurements. It is still necessary to have a reliable INS solution as the primary navigation source, and the secondary source is used to aid and correct the INS solution.

Velocity Aiding is a valuable technique for INS/GNSS navigators, especially in scenarios where GNSS signals are often blocked or unavailable. By integrating velocity measurements from a secondary source, VA improves the accuracy, reliability, and robustness of the navigation solution. It allows for continuous navigation even in challenging environments and provides a means to mitigate the drift and errors inherent in INS solutions. While VA is not a replacement for GNSS, it serves as a valuable backup and enhances the overall performance of INS/GNSS navigation systems, making them more suitable for a wide range of applications, including autonomous vehicles, unmanned aerial vehicles, and marine vessels.

Darren Fisher

Related Content