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INS-Aided GNSS

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Satellite navigation and inertial navigation are complementary and, therefore, natural allies. Global navigation satellite system (GNSS) receivers have astounding absolute positional accuracy, but are subject to signal loss or degradation, due to occultation (urban canyons, thick canopy, etc.), multi-path, jamming, and spoofing. Inertial navigation systems (INS) are low-noise and accurate in the short term: they provide an excellent dynamic response at very high data rates, produce data that is solely dependent on the motion of the platform and local gravity, and determine proper orientation (roll, pitch, and heading), but they are subject to drift. 

 

 

GNSS + INS Working in Tandem

By periodically re-initializing an INS, a GNSS receiver constrains its drift; conversely, the INS fills the gap when GNSS or GPS signals fail, and helps to remove measurement outliers mostly caused by multi-path. Thus, integrated GNSS-INS positioning, navigation, and timing (PNT) systems can benefit from the best of both worlds. From guided missiles to autonomous vehicles and pedestrian dead reckoning, many systems now maintain accuracy this way, allowing them to work even in tunnels and deep inside buildings, where signals would traditionally be disrupted. This method has the additional advantage of requiring less power and therefore boosting a device’s operating time, because the GNSS module can be kept in sleep mode most of the time.

The inertial measurement units (IMUs) used in INS are generally based on multi-axis combinations of precision gyroscopes, accelerometers, and magnetometers using algorithms to determine location, direction, and position. Gyroscopes measure the angular velocity; accelerometers measure overall acceleration; and magnetometers provide the direction of the magnetic field.

 

 

The Rising Popularity of Integrated Systems

Several recent trends have boosted both the demand and supply of integrated GNSS-INS PNT systems:

  1. GNSS Vulnerabilities. While the accuracy of GNSS receivers has increased, largely thanks to the growing number of GNSS satellites they can use (more satellites in the sky and more channels on the receivers), recent jamming and spoofing attacks on GNSS signals have exposed an alarming vulnerability, necessitating alternative and complementary navigation systems when GNSS signals become unavailable or untrustworthy.
  2. Safety Implications. The advent of advanced driver assistance systems and autonomous vehicles has made accurate and resilient navigation — especially, extremely precise heading — safety critical.
  3. Cost-Benefit Advantages. Inertial measurement units (IMUs) are ever more reliable, precise, and accurate, while also smaller, lighter, less power-hungry, and cheaper, mostly due to dramatic advances in micro-electrical-mechanical systems (MEMS) sensors.

 

 

Alternative Integrations and Their Limiting Factors

Signals other than GNSS are the key to positioning for both the transportation and machine control markets. While INS are emerging as the primary GNSS co-star, the Defense Advanced Research Projects Agency (DARPA) and commercial companies are developing many other solutions. One of them involves generating navigation information by matching measurements from a sensor to maps of terrain height, gravity, magnetic fields, Wi-Fi RSS, or other natural or manmade features. INS is often critical to the accuracy of these methods.

Integrated navigation systems for autonomous vehicles must balance two conflicting requirements: high accuracy and low cost, size, weight, and power (CSWaP). Traditionally, efforts to compensate for inevitable loss of GNSS signals have relied on sensors, such as cameras and lasers, which could violate CSWaP constraints and may not function in all weather conditions. A new trend is to research ways to supplement GNSS via signals of opportunity (SOPs), which are ambient radio signals not intended as PNT sources, such as cellular, Wi-Fi, AM/FM radio, digital television, and the Iridium satellite constellation.

Another approach is to integrate INS with LiDAR. When this is done for airborne applications, the free inertial navigation solution is used to create the point clouds, which are subsequently matched to a digital terrain elevation model (DEM). The results are fed back to the platform navigation filter, providing corrections to the free navigation solution, which may then be used to recreate the point cloud to obtain better surface data. Unfortunately, depending on the LiDAR data acquisition parameters, INS drift between the acquisition of the two point clouds could be significant, severely impacting the process.

 

 

Precise Results with GNSS-Aided INS

Multifrequency GNSS receivers with inertial components on a small, lightweight board can now typically deliver centimeter-accurate solutions. Ultimately, however, successful INS-GNSS integration requires an understanding of the application’s goals, including the required accuracy of attitude and position, and its environment, such as the severity of GNSS obstructions and the vehicle’s expected dynamics.

 

 

Furthering Integration with Sensor Fusion

GNSS-INS can also be paired with multiple sensors simultaneously — such as cameras, LiDAR, infrared, etc. — to produce what is known as seamless "perception and localizaiton," a nearly uninterrupted and detailed mapping of the environment and obstructions in a vehicle's immediate surroundings. Explore how this technique is being applied to autonomous vehicles in this interview with Autonomous Vehicle International.