Continuous tracking in Free Space Optical balloon mesh networks

This research investigates the broad use of state estimation for tracking purposes in free space optical balloon mesh networks. The Object tracking approaches have been studied extensively due to their wide range of applications in different fields. One of the methods to track an object is by estimating the object's state through observers and object's motion model. We propose a novel approach employing Kalman filter to implement an autonomous tracking system. The targets being tracked also act as sources of measurement input to track other non-stationary objects. The purpose is to find an efficient way to localize the mobile objects in free space through cooperative measurements input from neighbouring balloons in a free space balloon mesh network. We use Doppler effect to sense the Doppler shift at the receiver. We do extensive simulations to evaluate the performance of our proposed algorithms. Sensor data fusion is used for Extended Kalman Filter input from on-board sensors and remote sensors under two different scenarios. Simulation results show that the estimation and tracking algorithm proposed in this paper converges quickly.