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A Strapdown Interial Navigation System/Beidou/Doppler Velocity Log Integrated Navigation Algorithm Based on a Cubature Kalman Filter

机译:基于库伯卡尔曼滤波的捷联式内部导航系统/北斗/多普勒速度对数组合导航算法

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摘要

The integrated navigation system with strapdown inertial navigation system (SINS), Beidou (BD) receiver and Doppler velocity log (DVL) can be used in marine applications owing to the fact that the redundant and complementary information from different sensors can markedly improve the system accuracy. However, the existence of multisensor asynchrony will introduce errors into the system. In order to deal with the problem, conventionally the sampling interval is subdivided, which increases the computational complexity. In this paper, an innovative integrated navigation algorithm based on a Cubature Kalman filter (CKF) is proposed correspondingly. A nonlinear system model and observation model for the SINS/BD/DVL integrated system are established to more accurately describe the system. By taking multi-sensor asynchronization into account, a new sampling principle is proposed to make the best use of each sensor's information. Further, CKF is introduced in this new algorithm to enable the improvement of the filtering accuracy. The performance of this new algorithm has been examined through numerical simulations. The results have shown that the positional error can be effectively reduced with the new integrated navigation algorithm. Compared with the traditional algorithm based on EKF, the accuracy of the SINS/BD/DVL integrated navigation system is improved, making the proposed nonlinear integrated navigation algorithm feasible and efficient.
机译:带有捷联惯性导航系统(SINS),北斗(BD)接收器和多普勒速度测井(DVL)的集成导航系统可用于海上应用,因为来自不同传感器的冗余信息和互补信息可以显着提高系统精度。然而,多传感器异步的存在将错误引入系统。为了解决该问题,常规上将采样间隔细分,这增加了计算复杂度。相应地,提出了一种基于库伯卡尔曼滤波(CKF)的创新型组合导航算法。建立了用于SINS / BD / DVL集成系统的非线性系统模型和观测模型,以更准确地描述该系统。通过考虑多传感器异步,提出了一种新的采样原理,以充分利用每个传感器的信息。此外,在这种新算法中引入了CKF,以提高滤波精度。该新算法的性能已通过数值模拟进行了检验。结果表明,新的集成导航算法可以有效地减少位置误差。与传统的基于EKF的算法相比,改进了SINS / BD / DVL组合导航系统的精度,使得所提出的非线性组合导航算法既可行又高效。

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