Predicting next position of flying object by using Machine Learning

Description

In recent years, internal security has become a serious concern due to the advent of small weaponry systems like drones and rocketry systems. However, security agencies are working hard to keep us secure from external security threats, but internal security is also a key concern. Therefore, it is essential to develop and design security solutions that can deal with internal security issues based on drones and rockets. This project aims to predict the next position of a flying object in real time by applying machine learning algorithms. The method utilizes the existing sensor infrastructure randomly placed in a two-dimensional ground. The sensor hint of a two-dimensional area has been used for monitoring and tracking a moving object in three-dimensional space.

To develop a security solution for flying objects, it is required to consider the technique of object tracking. In this presented work, Wireless Sensor Networks (WSN) infrastructure has been supposed to prepare the method of flying object tracking. This method first utilizes the projection of three-dimensional space into two-dimensional space and then the trajectory of objects and sensor hints are used to calculate the next possible position of a flying object. Next, a deep learning algorithm is used to train on the different flying object trajectories. The trained Machine Learning algorithm is further used to predict the next position of the flying object. The experiments have been conducted and the results are discussed.

The simulation is implemented on Python technology and the prediction performance has been analyzed in terms of accuracy. The random movement of the object is planned to predict the next position of the flying object. In addition, the reaction time of the model is also measured, which is essential to know the efficiency of the model. finally, the conclusion based on the results has been discussed for estimating the speed and angle of movement. Additionally, the future extension plan has been discussed for making a more improved and well-featured system.