摘要
为解决复杂路况下车载组合导航系统存在的卫星导航系统信号衰弱、断续导致信号观测性差和组合滤波器稳定性下降甚至发散等问题,采用了一种简化的、易于工程实际应用的车载自适应组合导航算法,利用数据检测方法对卫导原始观测数据进行评估,根据评估结果构造自适应滤波因子,实时更新滤波器量测噪声协方差阵,提高滤波器对观测信息变化的适应能力。通过实际动态跑车试验,表明这种简化的自适应组合导航算法在卫导信号断续情况下,仍能保证3 m(RMS)的定位精度、0.04 m/s(RMS)的测速精度,较常规Kalman滤波定位精度提高近30%,测速精度提高达70%,能满足城市、山区等恶劣场景下车载导航的需求。
To solve the problem of worse GNSS （Global Navigation Satellite System） signal observation and decreased even diverged integrated filter stability caused by the signal attenuation and interruption for vehicle integrated navigation system in complex road environment,a simplified vehicle adaptive integrated navigation algorithm easily for engineering practical application was adopted. Raw GNSS observation data was evaluated by the data detection method, adaptive filter factor was constructed according to the evaluation results,filter measurement noise covariance matrix was updated in real time to improve the adaptive ability for the change of measurement information.According to actual vehicle moving field test,the simplified adaptive navigation system could guarantee a posi- tion accuracy of 3 m （RMS） ,and a velocity accuracy of 0.04 m/s （RMS）.The position accuracy is improved nearly 30% and velocity accuracy is improved up to 70% in contrast with Kalman filter when GNSS signal was interrupted.This approach can satisfy vehicle navi- gation requirement in bad environment such as city, mountain area and so on.
出处
《无线电工程》
2016年第12期43-46,82,共5页
Radio Engineering
基金
国家高技术研究发展计划("863"计划)基金资助项目(ZX151450001
2013AA122105)
关键词
车载导航
组合导航
自适应滤波
量测噪声协方差阵
vehicle navigation
integrated navigation
adaptive filter
measurement noise covariance matrix
