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针对非线性非高斯导航系统信息处理问题,采用自组织算法、神经网络和遗传算法等改进传统非线性Kalman滤波算法,构建一种自适应的组合导航系统。应用具有冗余趋势项的自组织算法、Volterra神经网络和遗传算法,建立导航系统误差的非线性预测模型,进而计算得到其预测值;将该预测值与Kalman滤波算法求得的估计值进行比较得到差值,以此监测Kalman滤波算法的工作状态;采用自适应控制方法,在导航系统结构层面改进Kalman滤波算法,构建新型的导航系统误差补偿模型。开展基于导航系统KIND-34的半实物仿真研究,应用所提出的改进方法改善了导航系统误差的补偿效果,提高了组合导航系统的自适应能力和容错能力。
Aiming at the problem of information processing in non-linear non-Gaussian navigation system, an improved adaptive integrated navigation system is proposed by using self-organizing algorithm, neural network and genetic algorithm to improve the traditional nonlinear Kalman filtering algorithm. The self-organizing algorithm with redundant trend items, Volterra neural network and genetic algorithm are applied to establish the nonlinear prediction model of navigation system error, and then the predicted value is calculated. The predicted value is compared with the estimated value obtained by Kalman filtering algorithm Get the difference, in order to monitor the working state of Kalman filter algorithm; Adopt adaptive control method, improve the Kalman filter algorithm in the navigation system structure level, build a new type of navigation system error compensation model. The semi-physical simulation based on the navigation system KIND-34 is carried out. The improved method is applied to improve the compensation effect of the navigation system error and improve the adaptability and fault tolerance of the integrated navigation system.