论文标题
基于重建的观察向量的罪恶/DVL的强大初始对齐
Robust Initial Alignment for SINS/DVL Based on Reconstructed Observation Vectors
论文作者
论文摘要
未对准角将导致多普勒速度日志（DVL）和跨式惯性导航系统（SINS）的整合发生相当大的误差。在本文中，提出了针对罪恶/DVL的强大初始对准方法来解决一个实际的适用问题，即DVL的输出通常被异常值损坏。首先，总结了罪恶/DVL的一致性原则。其次，基于该比对方法的原理，研究了表观速度模型，并详细介绍了表观速度模型的参数表达。使用明显的速度模型，通过开发的鲁棒卡尔曼滤波器（RKF）估算了表观速度模型的未知参数，然后由估计参数重建了重建的观测值，在该观察矢量中被检测到和分离。基于重建的观测向量，确定了初始态度。最后，进行了模拟和现场测试以验证所提出方法的性能。测试结果表明，所提出的方法可以有效地检测和隔离异常值并获得比以前的工作更好的性能。
Misalignment angle will result in a considerable error for the integration of Doppler Velocity Log (DVL) and of Strapdown Inertial Navigation System (SINS). In this paper, a robust initial alignment method for SINS/DVL is proposed to solve a practical applicable issue, which is that the outputs of DVL are often corrupted by the outliers. Firstly, the alignment principle for SINS/DVL is summarized. Secondly, based on the principle of this alignment method, the apparent velocity model is investigated, and the parameters expression of the apparent velocity model are derived detailed. Using the apparent velocity model, the unknown parameters of the apparent velocity model are estimated by the developed Robust Kalman Filter (RKF), then the reconstructed observation vector, where the outliers are detected and isolated, is reconstructed by the estimated parameters. Based on the reconstructed observation vectors, the initial attitude is determined. Finally, the simulation and field tests are carried out to verify the performance of the proposed method. The test results are shown that the proposed method can detect and isolate the outliers effectively and get better performance than the previous work.
