What is inertial navigation?
Inertial navigation (inertial navigation) is a technology that obtains instantaneous speed and instantaneous position data of an airplane by measuring its acceleration and automatic integration operation. Inertial navigation system equipment installed in the carrier, does not rely on external information, does not radiate energy, not easy to interfere, is an independent navigation system.
The so-called new generation navigation system of the U.S. Army is essentially a micro-inertial navigation system based on the latest technological achievements of modern atomic physics.
Inertial navigation system is one of the earliest navigation systems invented by mankind. Inertial navigation technology was first applied to V-2 rockets in Germany as early as 1942. The U.S. Department of Defense Advanced Research Projects Agency new generation navigation system mainly uses Newton's laws of motion to automatically calculate the instantaneous speed and position information of the carrier platform, through the integration of atomic gyroscopes, accelerometers and atomic clocks on the microchip, to accurately measure the angular rate of the carrier platform relative to the inertia of the space and the acceleration information, to provide accurate timing services for the carrier.
Data show that in 2003, the U.S. Department of Defense spent tens of millions of dollars to begin developing atomic inertial navigation technology. Once the technology is successfully developed, inertial navigation will achieve unprecedented accuracy. Specifically, it will be 100 to 1,000 times more accurate than the current most accurate military inertial navigation, which will revolutionize the field of military positioning and navigation.
Due to the small size, low cost, high accuracy, no dependence on external information, no radiation of energy, strong anti-jamming ability and good concealment of the navigation system, it is likely to become a substitute for GPS technology.
Second, inertial navigation applications
Inertial measurement devices, including accelerometers and gyroscopes, are also known as inertial navigation combinations. The former measures the acceleration of an object and the latter, also known as angular velocity sensors, measure angular velocity. Using the parameters of these devices, computation and navigation may seem simple, but long-term navigation remains difficult due to the fact that the sampling frequency is usually very high (tens or even hundreds of times per second) and the cumulative error can easily be amplified.
However, it is feasible to use it for a short period of time under certain preconditions.
In daily cell phone navigation, areas such as tunnels, viaducts, dense forest paths, and narrow lanes of high-rise buildings are often present. Navigation suddenly stops until the car drives to an open area, and the parking space icon in the navigation suddenly skips, which is a very bad experience.
When the position is lost, the navigation software knows the speed, vehicle position, driving route and other information. Combined with the acceleration provided by the acceleration sensor, it can calculate the displacement generated by the acceleration based on the secondary point, then the displacement generated by the speed based on the initial speed, and then calculate the latest position of the vehicle. In this way, it can still continue to navigate without GPS.
GPS Navigation
GPS navigation is a system that guides the user to drive according to the position information provided by GPS and the route planned before navigation.
It is well known that GPS calculates its position by receiving signals sent by satellites, and when GPS devices such as cell phones are blocked, the GPS device cannot locate the position.
Shielding can be a variety of situations, such as roofs, viaducts, houses, tunnels and so on. At this time, the possible location can be inferred by the relationship between speed, time and distance based on the speed calculated from the last GPS signal.
If the vehicle's speed does not change under tunnels and viaducts, this method may work well for navigation. But if you slow down, the parking spot on the map will quickly run to the end of the tunnel and then stop and wait. Conversely, if you speed up, the parking spot will go halfway and then suddenly jump out of the tunnel.
In order to solve these two situations, the following acceleration sensor device needs to be introduced. With acceleration, we can better infer the current speed to solve the above problems.
IV. Acceleration Sensor
An acceleration sensor is an electronic device that measures acceleration and converts it into an electrical signal. It uses Newton's second law A=F/M. The sensitive parts inside the sensor are deformed by force, and the corresponding acceleration signal is obtained by measuring its deformation and converting the relevant circuitry into a voltage output.
Calculate the distance of the acceleration of a cell phone. Acceleration is the rate of change of the object's velocity. Velocity is the rate of change of the object's position.
Using the concept of integration, we can conclude that for a signal sampling, if we can get the instantaneous value of the signal size, we can get a small region between two samples. The sampling time is the same (the width of the area) and then the sampled value (the height of the area) is obtained. We can calculate the sum of the areas by the following method. But it can be seen that there are errors.
We call these errors sampling losses. To minimize these errors, we need to score again: area 2 is a triangle and is half of the calculated area. The sum is the following formula.
This is a much smaller error than before.
There are positive and negative accelerations. Our sampling needs a standard value as a reference. The calibration value is the acceleration value measured without movement. There are considerations to be taken into account in practical applications:
The signal is somewhat noisy, so it must be digitally filtered. For example, a moving average algorithm can be used to process the average result of a certain number of sampled values.
Even with filtered data there are still errors, so another filter must be implemented to filter obviously problematic data.
Validation values must be available. Their calculation is critical to the overall result.
Positive and negative acceleration, positive and negative sign cannot be ignored.
The faster the sampling frequency, the more accurate the results, but power consumption, time and memory limitations need to be considered.
The sampling interval must be the same.
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