Autonomous vehicles in cities must operate in “urban canyons” caused by tall buildings that block Global Navigation Satellite System (GNSS) signals. With GNSS blocked, the autonomous navigation system cannot rely on the satellite-based system for corrections. In these cases, a high-performance inertial navigation solution capable of providing accurate real-time positional data is essential to maintain the vehicle’s navigation performance during these times of GNSS unavailability.
In navigation applications, Bias Stability is a critical performance parameter because the rate output of the gyro is constantly mathematically integrated. Therefore, the error is cumulative during the mission time. Additional sources of error that are statistically dominated by white noise integrate to zero. Bias noise does not integrate to zero, making Bias Stability a large contributor of error in autonomous guidance systems.
What is Bias Stability?
Bias Stability (also known as Bias Instability) can be defined as how much deviation or drift the sensor has from its mean value of the output rate. Essentially, the Bias Stability measurement tells you how stable the bias of a gyro is over a certain specified period of time. Lower Bias Instability is advantageous, as it results in a gyro producing fewer deviations from the mean rate over time.
In autonomous vehicles, the gyroscope requirements are focused on having a low Bias Instability, which is required to handle the demands of autonomous vehicles operating in a variety of conditions while maintaining high safety margins. These conditions include operation in GNSS-denied environments, such as crowded cities filled with urban canyons.
One of the reasons that a FOG has lower Bias Instability is because it’s a solid-state sensor with extremely low measurement noise over a high dynamic range. A FOG doesn’t generate any acoustic vibration and doesn’t have any moving components. Due to its inherent low noise, FOG technology is one of the few technologies able to cope with applications demanding the highest performance. Contrast this with Micro-Electro-Mechanical Systems (MEMS) gyro technology which uses the Coriolis effect based on vibrating mass deflection resulting from rotation. The bias of a MEMS gyro will wander over time due to flicker noise in the electronics and other effects.
FOG technology provides for low Bias Instability gyros that cannot be realized by MEMS-based technology due in part to the silicon or quartz vibrating beam and detection electronics used in MEMS gyros. Too much gyro noise results in loss of application precision and accuracy in the rate or position measurement.
Bias stability is commonly used to determine gyro grades with tactical grades offering bias stability of 0.1°/hr., and navigation grades delivering bias stability of 0.01°/hr. KVH offers a variety of FOGs that range from tactical grade to navigation grade. These gyros are combined with accelerometers in our Inertial Measurement Units (IMU), and with GNSS receivers and magnetometers in KVH’s inertial navigation systems (INS). Inertial navigation systems that use FOGs offer far greater precision when navigating during periods of GNSS outage.
Another advantage of KVH FOGs and inertial systems in addition to low bias stability is that KVH IMUs and INS feature software capability to integrate successfully with sensors such as GPS and speed sensors via advanced non-linear estimation methods. These sensors form a subsystem in the autonomous navigation system which is then integrated with other sensor packages, to include LiDAR and cameras.
Combining several types of sensors into a navigation solution is defined as “sensor fusion.” Sensor fusion enables the navigation system to adapt and assign values to the different sensors available, and then to utilize the sensors as the changing situation demands.
For more about the challenges of navigation with GPS-denial, read about how spoofing was tested on the superyacht, White Rose of Drachs.
Explore the challenges facing GPS/GNSS navigation when you download the free eBook, Meeting the Growing Threat of GPS/GNSS Disruption.
Editor’s Note: This blog was updated to correct several references to bias instability. Sorry for any confusion! (10/31/19)