Unmanned Aerial Vehicle State Estimation Based on Square Root Lattice Tobit Kalman Filter

The state estimation methods for nonlinear systems with censored measurements are proposed. Firstly, the Tobit-II type censored measurements equation are constructed for nonlinear system. Secondly, lattice sampling is employed to predict the latent measurement. Subsequently, the Square Root Lattice Tobit Kalman Filter (RLTKF) is derived using the square root covariance matrix obtain via QR decomposition. Finally, the algorithms are validated through the Unmanned Aerial Vehicle (UAV) cruising motion scenario, demonstrating high estimation accuracy and better robustness.