A reconfigurable FPGA framework for data fusion in UAV's.

This paper presents the results of an effort to develop a reconfigurable helicopter platform capable of autonomous flight using a reconfigurable FPGA framework to fuse the data from sensors, Readings from accelerometer and absolute GPS location data is fused using Kalman filter to combine the low frequency stability of GPS systems with the high frequency tracking of accelerometers thus achieving stable static and dynamic three-degree-of-freedom tracking for the use of an UAV onboard navigational system. The sensor data can have different data rates and noise figures. Simulation was done on data fusion of noisy GPS and accelerometer data using Kalman filter. The results showed the data fusion of the sensors gives a better positional and velocity estimates decreasing the average errors of positional and velocity estimates by more than 50%. More over the fused data does not accumulate error over time, as compared to positional and velocity estimates obtained earlier.