AUV模型辅助捷联惯导组合导航方法研究
发布时间：2018-08-20 14:44
【摘要】：捷联惯性导航系统(SINS,Strapdown Inertial Navigation System)作为自主式水下潜器(AUV,Autonomous Underwater Vehicle)的主要导航方式,在没有有效辅助的情况下,由于误差积累引起的捷联惯导系统发散问题,多采用多普勒测速仪(DVL,Doppler velocity Log)对其漂移进行限制。然而,探测方法较为粗糙,水下地形复杂等问题使得某些情况下DVL的探测范围无法到达海底,降低了 SINS/DVL组合导航模式的可行性。在DVL失效,无法得到准确量测时,捷联惯导系统误差迅速增大,导航精度大大降低。同时,系统模型失真,噪声统计特性不确定,将会导致卡尔曼滤波精度降低,严重时会出现滤波发散的情况。因此,需要一个能有效降低漂移的导航方式对SINS的速度信息和位置信息进行校正,并且需要鲁棒性较好的滤波器对该组合导航系统进行状态估计。针对以上问题,本文提出了采用描述AUV运动的数学模型辅助捷联惯导的组合导航方法,并且选用渐消记忆卡尔曼滤波和H∞滤波对模型辅助的组合导航系统进行状态估计。本文详细介绍和深入研究了以下内容:首先,本文分析了传统的组合导航方式和模型辅助的组合导航方式之间的区别;介绍了捷联惯导的原理、机械编排、进行了误差分析,并给出了捷联惯导的误差方程。其次,本文根据AUV运动的模型及海流对运动模型的影响,建立在海流影响下的AUV运动的数学模型,利用其三自由度模型、合外力及力矩的数据解算得到AUV的位置信息和速度信息。然后,结合对渐消记忆滤波的深入研究提出了改进的渐消记忆卡尔曼滤波算法,并将其应用到模型辅助组合导航系统中,在模型准确和不准确的情况下分别进行了匀速直线运动和变速运动的仿真,仿真结果表明渐消记忆滤波算法可以改善模型辅助捷联惯性组合导航系统精度,且在模型不准确时抑制卡尔曼滤波发散。最后,采用了鲁性更好的H∞滤波算法对模型辅助捷联惯导组合导航系统进行状态估计,在模型准确和不准确的情况下分别进行了匀速直线运动仿真和变速仿真,仿真结果表明H∞滤波算法不但可以改善模型辅助捷联惯导组合导航系统精度还可以提高系统鲁棒性,且在模型不准确时可以抑制卡尔曼滤波的发散。本文的研究结果表明改进的渐消记忆卡尔曼滤波在AUV模型辅助捷联惯导组合导航系统中的应用可以有效的抑制SINS发散,提高组合导航精度。H∞滤波在模型辅助组合导航系统中的应用能够有效提高组合导航系统精度和鲁棒性。该组合导航系统可以作为DVL工作失效时的备份导航系统,且这两种滤波方式能够有效抑制模型不准确的情况下卡尔曼滤波的发散问题。
[Abstract]:As the main navigation mode of autonomous Underwater Vehicle), sins Strapdown Inertial Navigation System) is a problem of divergence of sins caused by error accumulation in the absence of effective assistance. Doppler velocimeter (DVL) is often used to limit the drift. However, some problems such as rough detection method and complex underwater terrain make the detection range of DVL can not reach the bottom of the sea under some circumstances, which reduces the feasibility of SINS/DVL integrated navigation mode. When DVL fails and cannot be measured accurately, the strapdown inertial navigation system error increases rapidly and the navigation accuracy decreases greatly. At the same time, the distortion of the system model and the uncertainty of the statistical characteristics of noise will lead to the reduction of Kalman filtering accuracy and the occurrence of filtering divergence in serious cases. Therefore, a navigation method that can effectively reduce drift is needed to correct the velocity and position information of SINS, and a robust filter is needed to estimate the state of the integrated navigation system. In order to solve the above problems, this paper proposes a mathematical model to describe the motion of AUV in sins integrated navigation, and uses fading memory Kalman filter and H 鈭，
本文编号：2194010
[Abstract]:As the main navigation mode of autonomous Underwater Vehicle), sins Strapdown Inertial Navigation System) is a problem of divergence of sins caused by error accumulation in the absence of effective assistance. Doppler velocimeter (DVL) is often used to limit the drift. However, some problems such as rough detection method and complex underwater terrain make the detection range of DVL can not reach the bottom of the sea under some circumstances, which reduces the feasibility of SINS/DVL integrated navigation mode. When DVL fails and cannot be measured accurately, the strapdown inertial navigation system error increases rapidly and the navigation accuracy decreases greatly. At the same time, the distortion of the system model and the uncertainty of the statistical characteristics of noise will lead to the reduction of Kalman filtering accuracy and the occurrence of filtering divergence in serious cases. Therefore, a navigation method that can effectively reduce drift is needed to correct the velocity and position information of SINS, and a robust filter is needed to estimate the state of the integrated navigation system. In order to solve the above problems, this paper proposes a mathematical model to describe the motion of AUV in sins integrated navigation, and uses fading memory Kalman filter and H 鈭，
本文编号：2194010
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