Multi-model Kalman filter algorism based on GA
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摘要: 基于长航时无人机惯性/天文/卫星(INS/CNS/GPS)组合导航系统模型,针对复杂环境所引起的系统模型参数变化导致单一固定参数滤波器精度降低问题,提出了一种基于遗传算法的多模型自适应Kalman滤波算法,并与单一模型下的Kalman滤波器方法进行了比较.仿真结果表明,与采用单一模型的Kalman滤波算法相比,该方法不仅能大大提高导航系统的精度和可靠性,而且还可以较好地辨识出组合导航系统惯性器件噪声统计模型参数.Abstract: A multi-model Kalman filter based on GA(genetic algorism) was designed. When the parameter is imprecise, estimation results of Kalman filter are false.Method used in this paper can estimate the best parameters on line,so as to get true estimation results.At the end of this paper the application to integrated navigation system was studied with simulation. The results of simulation show that as compared with single-model algorithm, this method not only can improve accuracy and reliability of integrated navigation system greatly,but also can identify the noise statistical models of inertial elements in the integrated navigation system relatively well.
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