Simulation target tracking by using EKF process and multi-agent RL in omnet++
Budget: £20 – £250 GBP
o We plan to implement the Extended Kalman Filter (EKF) process for trajectory prediction procedure in OMNET++.
o We plan to extend the implementation of multi-agent reinforcement learning (RL) network for sensor scheduling during target tracking in OMNET++.
o The UAVs cooperate with each other in order to learn how to share their sensory data and act as the scout for each other.
o Utilizing a multi-agent RL network makes possible for the UAVs to communicate and share their capabilities and learn from one another in OMNET++.
o We plan to extend the implementation of multi-agent reinforcement learning (RL) network for sensor scheduling during target tracking in OMNET++.
o The UAVs cooperate with each other in order to learn how to share their sensory data and act as the scout for each other.
o Utilizing a multi-agent RL network makes possible for the UAVs to communicate and share their capabilities and learn from one another in OMNET++.
Related categories:
C Programming
Matlab and Mathematica
C++ Programming
Deep Learning
Edge Computing