提高卫星导航精度的卡尔曼滤波算法应用研究

采用卫星导航对海上航行的船舶进行速度测量是目前最广泛应用的方法,但是卫星导航接收机由于受外界偶然因素的影响,其显示的速度往往包含噪声,且属于随机的高斯白噪声。针对此问题,提出一种基于卡尔曼滤波理论的计算模型,只考虑上一个时刻和当前时刻的关系,大大地减少数据冗余;应用迭代的方法来处理卡尔曼滤波,以更好地简化计算的过程,并根据深圳某航海公司的实际数据对此计算模型进行仿真验证。实验结果表明,对于数据跳跃十分大且明显的噪声数据,经过滤波后,这些数据变得更加平滑和准确。

Application of Kalman filter algorithm to improving the accuracy of satellite navigation

Satellite navigation is the method widely used to measure the speed of ships sailing on the sea.However,due to the influence of external accidental factors,the speed displayed by satellite navigation receiver often contains noise which belongs to random Gaussian white noise.To solve this problem,this paper proposes a calculation model based on Kalman filter theory concerning the relationship between the previous time and the current time,which greatly reduces the data redundancy.The iterative method is used to deal with the Kalman filter which can better simplify the calculation process.The calculation model is verified through the simulation based on the actual data of a navigation company in Shenzhen.The experimental result shows that:for those with very large jump after being filtered,these data become smoother and more accurate.