I. Inertial Sensors
Inertial navigation systems (INS) are a navigation technology that has developed alongside inertial sensors. Their advantages, such as complete autonomy, immunity to interference, large output information volume, and strong real-time output, have led to their widespread application in military aircraft and related civilian fields. The accuracy and cost of an INS primarily depend on the accuracy and cost of the gyroscope and accelerometer. In particular, the drift of the gyroscope has a cubic effect on the increase of the INS' position error as a function of time. High-precision gyroscopes are difficult to manufacture and very expensive. Therefore, the inertial technology community has been constantly seeking effective methods to improve the accuracy of gyroscopes while reducing system costs.
Miniature mechanical inertial navigation sensors will dominate applications with tactical performance requirements (or lower). The military market will drive the development of these sensors, such as those for smart aircraft, autonomous guided missiles, short-range tactical missile navigation, fire control systems, motion compensation for radar antennas, composite intelligent miniature thrusters, and chip-sized INS/GPS systems. The development of strategic guidance systems for intercontinental ballistic missile systems and submarine-launched ballistic missile systems will depend on the overall performance requirements of weapon and strategic systems. Navigation systems will continue to employ stabilized platform mechanical gyroscopes and accelerometers (pendulum gyro-accelerometers) to improve navigation accuracy.
From liquid-floating gyroscopes in the 1950s to dynamically tuned gyroscopes in the 1970s; from ring laser gyroscopes and fiber optic gyroscopes in the 1980s to vibrating gyroscopes in the 1990s, and the more widely reported microelectromechanical system (MEMS) gyroscopes, the development of inertial sensors has been continuously propelled forward. Therefore, research on inertial sensors has always been a key focus in the field of inertial technology worldwide. Various new materials and technologies have been incorporated into inertial sensor research. With the emergence of low-cost, high-precision inertial sensors, inertial navigation systems will become universal and low-cost navigation systems.
II. How inertial sensors are used in sensor fusion
Before we dive into sensor fusion, a quick recap of inertial measurement units (inertial sensors) seems relevant. An inertial sensor is a type of sensor that typically consists of an accelerometer and a gyroscope, and sometimes a magnetometer. By examining data from these sensors, a device can gain a more comprehensive understanding of its orientation and motion.
An accelerometer measures acceleration (change in velocity) in a single direction, such as the force you feel when you press the accelerator pedal of a car. When stationary, an accelerometer measures gravity.
A gyroscope measures the angular velocity around its three axes. At any given moment, it outputs its rotation, yaw, pitch, and roll.
It's quite simple: a magnetometer measures magnetic fields. By properly calibrating in a stable magnetic field, it can detect fluctuations in the Earth's magnetic field. Using these fluctuations, it finds the vector pointing to the Earth's magnetic north and assigns it an absolute heading.
The sensor information is then used to maintain the drone's balance, improve the heading of a home robot vacuum cleaner, change the orientation of a smartphone screen, and for other motion-related applications.
Now that we understand the components of an inertial sensor, its relationship to sensor fusion, and why we should care about it, well, sensors alone aren't that "intelligent." They generate raw data. But this raw data must be processed and packaged to become actionable.
The sensors in an inertial sensor are like specialists reading your patient files—they all have their own opinions, and their expertise gives them insights that others don't, but you can process their opinions to make a final decision. For example, if an accelerometer indicates that gravity is changing from pointing downwards to a more horizontal angle, but a gyroscope shows almost no movement, which do you believe? In this case, the gyroscope should be trusted more because it is unaffected by external forces. Since the gyroscope tells us that the user's frame hasn't changed, it's safe to say that the device has been accelerating, just like a car traveling in a straight line.
In another scenario, if the gyroscope displays a small and consistent angular velocity, but the accelerometer and magnetometer show the device is stationary, then you can trust the opinions of these two agreeing "doctors." You can then infer that some gyroscope bias is giving incorrect output. These examples are intended to demonstrate that sensor fusion is essential for understanding the optimal output based on sensor information fusion. This can be used to determine accurate motion, orientation, and heading information.
