【摘要】 Kalman滤波器是组合导航中最常用的最优滤波工具,但是在组合导航系统中有一些应用的局限性,尤其在低成本的GPS (GlobalPositioningSys tem) /DRS (DeadReckoningSystem)组合导航系统中,存在着使用的GPS接收机和惯导测量元件的精度不够高的问题,要提高系统的测量精度,只能提高算法软件的先进性.为补偿卡尔曼滤波发散的缺陷,将神经网络和遗传算法组成的混合算法与卡尔曼滤波相结合,应用到GPS/DRS组合导航系统中,该算法不仅具有普通神经网络的自主学习能力、好的实时性,还克服了传统算法收敛速度慢、对学习参数敏感、局部有极小点等缺点,同时兼具卡尔曼滤波的最优估计性能.仿真结果验证了这种算法和常规卡尔曼滤波算法相比较具有更高的精度和稳定性,经过对仿真数据进行统计分析,纬度误差的最大值降低了一个数量级.更多还原

【Abstract】 Kalman filter is the most usual optimization filter with some limitation when applied in the integrated navigation system. Especially in simple global positioning system/ dead reckoning system (GPS/DRS ),the receiver and the inertial navigation units are low-cost and low-accuracy. To improve accuracy of the system, it must be focused on the advanced algorithm. To compensate the divergence of Kalman filter, a hybrid algorithm composed of back propagation (BP) neural net and genetic algorithm (GA) together with Kalman filter was applied in low-cost GPS/DRS integrated navigation system. This algorithm owns not only self-study ability and good real-time performance of neural net but also optimization assessment ability of Kalman filter, and even overcomes many flaws of neural net,such as slow convergence ,sensitivity about the study parameters and local extremums. The simulation result also proves that this algorithm is prior in precise and stability compared to usual Kalman filter, for example, the statistic analysis shows that the maximal error of latitude is reduced to a lower magnitude.更多还原