Big data tracking and automatic measurement technology for unmanned aerial vehicle trajectory based on MEMS sensor

The usage of unmanned aerial vehicles (UAVs) is rapidly increasing in the current era as these devices are capable enough in providing unique solutions in applications such as inspection of environment, identification of disaster, rescue operations, and defense systems. For the governance of the flight missions in a complex defense environment, the usage of these systems necessitated a sound command over data mining process. The large volume of data is generated by UAVs and processing of this data is a challenging issue. The existing data tracking and management systems are expensive and complex. For defense systems, smarter solutions are needed to process the large volume of data at low cost and with high accuracy. Therefore, a technique of tracking the data generated by UAV and automatic measurement of trajectory based on micro-electro-mechanical systems sensor in UAV has been proposed in this paper to provide inexpensive solutions to overcome the problems of the existing data tracking and processing systems. An iterative learning control algorithm is utilized in UAV to ascertain the disturbance and modeling errors. The finest characteristics of the Kalman filter technique are used for estimations of UAV trajectory. The quadratic performance function is introduced in discrete equation to solve the model error disturbance. Then on the basis of gyroscope data, the quaternion differential equation is formulated. The gradient descent process is also used to speed up the processing of UAV data. The results depict that the proposed technique has the lowest data tracking error of the UAV trajectory (0.09%) and has good measurement accuracy of 92%. The proposed method also reduces time complexity and searches the solution space in a faster manner.