Background Information Drone NavigationA drone, or an unmanned aerial vehicle, is an unmanned aircraft or ship guided by remote control or onboard computers.
In recent years, drones have become increasingly popular, extending out of its military uses into industrial and entertainment uses. Some of these examples include Amazon Prime Air, which delivers packages directly to people’s’ homes and wildlife monitoring which record videos of animals without disturbing them. The great amount of uses of drones highlight the importance in researching in them. Drone Navigation, is used to understand where the drone is currently in 3D spaces. It uses large amount of data from various sensors most importantly, accelerometers, inertial measurement units(IMU) and tilt sensors to understand its position, acceleration and alignment relative to its environment. But, the raw data itself is quite useless as it is neither accurate nor precise.
This is due to uncontrollable factors like wind speed and air pressure and imprecise instruments, leading to inaccurate and noisy results. A solution to this problem is Kalman filtering. By using data collected from sensors as well as prediction models designed to simulate the drone’s behaviour the current state of the drone can be more accurately estimated. Figure 2: A droneWhat is a Kalman Filter ?Kalman filtering, named after its primary developer, Rudolf E. Kálmán, is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone. The Kalman filter plays an important role in many applications as we include sensing of the real world in our systems. A well-known application is for guidance, navigation, and control of vehicles, particularly aircraft and spacecraft.
The Kalman filter also works for modeling the central nervous system’s control of movement. Due to the time delay between issuing motor commands and receiving sensory feedback, usage of the Kalman filter supports the realistic model for making estimates of the current state of the motor system and issuing updated commands.The algorithm works in a two-step process.
In the prediction step, the Kalman filter produces estimates of the current state variables, along with their uncertainties. Once the outcome of the next measurement (necessarily corrupted with some amount of error, including random noise) is observed, these estimates are updated using a weighted average, with more weight being given to estimates with higher certainty. The algorithm is recursive. It can run in real time, using only the present input measurements and the previously calculated state and its uncertainty matrix; no additional past information is required.Extensions to the method have also been developed. An example of this is the extended Kalman filter, which work on nonlinear systems. This version of the Kalman filter linearizes about an estimate of the current mean and covariance.
In the case of well defined transition models, like navigation systems and GPS , the EKF has been considered the standard in the theory of nonlinear state estimation. Figure 2Research Hypothesis:It is expected that the Kalman filter estimation will be closer to a prior than the direct data collected from the drone’s sensors. Kalman filters integrate the data collected from the IMU, accelerometers and the tilt sensors using a fusion method in order to make an accurate and reliable estimate of the drone’s position.