摘要
卡尔曼滤波是一种递推线性最小方差估计算法,在组合导航数据融合中得到了广泛应用;但由于卡尔曼滤波要求系统模型和噪声统计特性精确已知,而实际中很难做到,因此常常出现滤波发散现象。鉴于此,着重研究了衰减记忆自适应卡尔曼滤波和渐消记忆卡尔曼滤波在INS/GPS组合导航中的应用,并通过对仿真数据的处理,验证了两种滤波算法在抑制滤波发散、提高组合导航系统精度和稳定性方面的可行性。
Kalman lter is a cursive minimum variance estimation technique,and has been widely used in combined navigation data fusion.However,Kalman lter requires the system’s model and noise statistical characteristics to be accurately known,and it is diffcult to do so in practice.erefore,Kalman lter divergence often occurs.In this paper,we studied the application of adaptive Kalman lter and fading memory Kalman filter in INS/GPS combined navigation.And then,we veri ed the feasibility of these two lter algorithms in suppressing the divergence of filter and improving the precision and stability of the combined navigation system through processing the simulation data.
作者
谭攀
伍仲南
康跃耀
TAN Pan;WU Zhongnan;KANG Yueyao
出处
《地理空间信息》
2019年第9期109-112,I0003,共5页
Geospatial Information
关键词
组合导航
卡尔曼滤波
渐消记忆
combined navigation
Kalman filter
fading memory
