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An Integrated Navigation Algorithm for AUV Based on Pseudo-range Measurements and Error Estimation

机译:基于伪距测量和误差估计的AUV组合导航算法

摘要

The Long Baseline (LBL) system is very important to precisely localize autonomous underwater vehicles (AUVs) close to the sea bottom in deep water. But the calibration error of the LBL beacons and the pseudo-range measurement caused by the motivation of the AUV, both them dilute the accuracy of LBL acoustic position. This paper proposes a quantitative description of the calibration error of beacons and pseudo-range how they reduce the LBL acoustic position precise. And then this paper proposes an integrated navigation algorithm for AUV, which integrates inertial navigation and LBL acoustic location to reduce the influence of the calibration errors of beacons and pseudo-range by online calibrating the beacon location errors and compensating the motivation of AUV to pseudo-range model. We name this algorithm as Pseudo-range and SLAM of beacons EKF (P-SLAM EKF). Through the sea trials of Qianlong-1, our algorithm proves very reliable and effective.
机译:长基线(LBL)系统对于精确定位深水海底附近的自动驾驶水下航行器(AUV)非常重要。但是,LBL信标的校准误差和AUV的动力引起的伪距测量,都降低了LBL声位的精度。本文对信标和伪距的校准误差提出了定量的描述,它们如何精确地降低了LBL声学位置。然后,本文提出了一种用于水下航行器的集成导航算法,该算法将惯性导航和LBL声学定位相结合,通过在线校正信标位置误差并补偿水下航行器对伪卫星的动力来减少信标和伪距校准误差的影响。范围模型。我们将此算法命名为信标EKF(P-SLAM EKF)的伪距和SLAM。通过乾隆一号的海试,证明了该算法的可靠性和有效性。

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