Moving Horizon Estimation for Moving Long Baseline based on Linear Positioning Model

This paper presents the real-time moving horizon estimation (MHE) for Moving Long Baseline (MLBL) positioning system. By establishing the linear positioning model, we formulate MLBL positioning problem in a linear, time-invariant, discrete-time system. In this system, we assume that the velocity of Autonomous Underwater Vehicle (AUV) is known, and the distances between Unmanned Surface Vessels (USVs) and AUV are the measurements. Then, we design a moving horizon estimator with the dynamic model and state constrains are considered. This estimator determines the position of AUV by solving a constrained optimization problem. The estimated positions are computed by minimizing the cost function. Simulation results demonstrate the performance of the MHE algorithm with the comparison of Kalman Filter (KF).