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A GNSS/INS Integrated Navigation Algorithm Based on Kalman Filter

机译:基于卡尔曼滤波的GNSS / INS组合导航算法

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GNSS/INS (Global Navigation Satellite System/ Inertial Navigation System) integrated navigation system can be applied to agricultural UAV (unmanned aerial vehicle) with the following two requirements: (1) After working for a long time, the precision of navigation parameters will not decrease; (2) The integrated navigation algorithm is simple and reliable, which requires low processing capacity for airborne chips. Aiming at satisfying above two requirements, firstly, the centralized Kalman filter method is used to fuse GPS (Global Position System) and INS systems under the premise of loose coupling. The combination is compact, which greatly reduces the amount of computing in the system and simplifies the complexity of the system. Secondly, the error of INS system navigation parameters estimated by discrete Kalman filter algorithm is fed back into INS system by feedback emendation method, which overcomes the problem that the navigation accuracy will decline after long time work. Finally, the simulations of velocity and position error after filtering are demonstrated respectively. The stability and effectiveness of proposed algorithms are verified.
机译:GNSS / INS(全球导航卫星系统/惯性导航系统)组合导航系统可用于满足以下两个要求的农业无人机(无人机):(1)长时间工作后,导航参数的精度将不高。减少; (2)集成导航算法简单可靠,对机载芯片的处理能力要求低。为了满足上述两个要求,首先,在松散耦合的前提下,采用集中式卡尔曼滤波方法融合GPS(全球定位系统)和INS系统。该组合是紧凑的,这大大减少了系统中的计算量,并简化了系统的复杂性。其次,采用反馈修正法将离散卡尔曼滤波算法估计的惯导系统导航参数误差反馈到惯导系统中,克服了长时间工作后导航精度下降的问题。最后,分别说明了滤波后速度和位置误差的仿真。验证了所提算法的稳定性和有效性。

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