A Novel Physics-Informed Recurrent Neural Network Approach for State Estimation of Autonomous Platforms

State estimation of autonomous platforms is a crucial element in the design and test of perception algorithms. The nonlinear nature of autonomous platforms makes hard to design accurate state estimation algorithms without using linearization techniques, large amount of data or knowledge of the physical parameters of the platform. This paper reports a novel state estimation algorithm of autonomous platforms. The proposed approach is based on a physics informed recurrent neural network (PIRNN) that combines the power of recurrent nets with an estimate structure of the autonomous platform model. This estimated model regularises the weights’ manifold of the network for the accurate estimation of the states. Boundedness of the proposed PIRNN is verified using Lyapunov stability theory as long as the physics-informed signals satisfy a persistent of excitation condition. Simulations are conducted to test the PIRNN model and show its benefits and challenges.