Robust Optimization-Based Alignment Method Based on Projection Statistics Algorithm

Due to the complex underwater environment, the assisted velocity provided by Doppler velocity log (DVL) may be contaminated by non-Gaussian noise. This will degrade the performance of the traditional optimisation-based alignment (OBA) method. To solve this problem, this paper proposes a robust OBA (ROBA) based on projection statistics (PS) algorithm. The ROBA contains two main steps which are also the contributions of the work presented here. Firstly, ROBA makes use of PS algorithm to identify the stored abnormal auxiliary velocity accurately under the framework of Kalman filter (KF), and then a robust strategy of the observation vector is designed to achieve the robustness of OBA. The semi physical experiment of SINS underwater in-motion coarse alignment is carried out based on the ship-mounted measured data. The experimental results demonstrate the superiority of the proposed method over the traditional ones, and the yaw alignment accuracy of ROBA is at least 30% improvement over the traditional ones under the non-Gaussian conditions.