Sensor Fusion

Purpose

In the designed system there are some environment sensing methods that give the same information about the environment. It can be proven that measurement updates can increase accuracy of a probabilistic function. As an example, Localization block can use UltraWide band and acceleration sensors to localize the position of drone. This data fusion is desirable because the UltraWide band system has a high accuracy but low response time, and acceleration sensors have lower accuracy but a higher response time. The other system that can benefit from sensor fusion is the psi angel block. The psi angel is needed for drone motion control and also for other detection blocks. The data of the psi angle are coming from the drone’s magneto meter and gyroscope. The magneto meter gives the psi angle with a rather high error. The gyroscope gives the derivation of the psi angle. The data provided by the gyroscope has a high accuracy but because it is derivation of psi angle the uncertainty increases by time. Sensor fusion can be used in this case to correct data coming from both sensors.

Method

There are several sensor fusion methods available. We tested Bayesian algorithm and Kalman filter algorithm. The Kalman filter appeared to be more robust and gave better results. It is important to mention that the Kalman filter practically does the optimization in the innovation state, when it compares the output prediction to the sensor measurements. For the angle data fusion the x(k) in Kalman filter is: [math]\displaystyle{ x(k│k-1)=F(k)x(k-1│k-1)+G(k)u(k) }[/math] In which [math]\displaystyle{ \ \ \ \ \ \ \ A=\begin{pmatrix} \theta \\ \theta_dot \end{pmatrix} }[/math]