一种基于机载无源北斗系统的改进卡尔曼滤波算法

针对广泛研究的无源北斗定位系统,由于定位接收机本身不精确、环境噪声以及人为干扰等因素的影响,而导致相应的量测序列出现某些粗大的错误数据。这些数据在数量级上与正常量测值的差值较大,其差值超过了允许的误差范围,它们会明显影响卡尔曼滤波过程和降低系统的定位精度,造成系统的不稳定甚至滤波发散。提出了一种利用卡尔曼滤波剔除动态野值改进的算法,该方法提高了卡尔曼滤波器在机载环境下的跟踪能力,由于降低了状态方程的维数,滤波的实时性得到提高,经试飞数据验证,由该算法滤波后无源北斗系统的定位精度可以达到92 m,能够满足一般空中用户的需要。

An Improved Kalman Filter Algorithm Based On Airborne BeiDou Passive System

The region passive locating system,named Beidou,nowadays is under extensive research,the inaccuracy of receiver,environmental noise,man-made interference and other factors will cause false data in the relevant measuring arrays.The difference between these data and the normal value in terms of quantity is larger and exceeds the allowed range of error,which will obviously affect the Kalman filtering process and cause the decrease of the position precision.All these will lead to instability of the system and even the disperse of filtering.This paper presents an improved algorithm,the principle of which is to reject the dynamic fault tolerant by using Kalman filter.This method upgrades the tracing ability of the filter under the airborne environment.The real-time character is improved because of the decrease in the dimension numbers of the state equation.Verified from the trying out data,the locating precision of the system can reach up to 92 meters,filtered by the algorithm,thus it can meet the general needs of the users in the air.