摘要
卡尔曼滤波(KF)在SINS+GPS组合制导方法中应用广泛,但是运载体的传感器数学模型一般为非线性,其模型复杂度和运算量较大。文章提出了SINS+GPS组合制导中基于误差状态量的卡尔曼滤波算法,为了回避非线性所带来的复杂性,将GPS数据与惯性导航数据的误差作为状态量,由于误差的数学模型为线性,明显降低了设计难度。文中给出了设计过程和导航系统的数学模型,对该模型的状态参数进行了设置,最后用仿真试验证明了该算法的有效性。
The introduction of the full paper explains what we believe to be an effective Kalman filtering algorithm, which is presented in sections 1 and 2. Their core consists of： （ 1 ） in order to reduce the non-linearity, complexity and large computational load of the mathematical model of sensors, we use the errors between GPS data and inertial navigation data as the state variables; because the mathematical model of the errors is linear, the design difficulty is greatly reduced ; （2） we establish the mathematical model of the SINS ＋ GPS navigation system, set the error state variables of the model and apply the filtering results to the adjustment of the SINS navigation system with feedback design. Section 3 simulates our Kalman filtering algorithm ; the simulation results, presented in Figs. 2 and 3, and their analysis show preliminarily that our Kalman filtering algorithm is indeed effective for minimizing the effect of the errors of the sensors.
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2011年第5期685-689,共5页
Journal of Northwestern Polytechnical University
关键词
算法
分析
设计
误差模型
反馈
全球定位系统(GPS)
惯性导航系统
卡尔曼滤波(KF)
模型
参数估计
传感器
仿真
捷联式惯性导航系统(SINS)
algorithms, analysis, design, error model, feedback, global positioning system, inertial navigation systems, Kalman filtering, models, parameter estimation, sensors, simulation, SINS（ strapdown inertial navigation system）
