摘要
自学习最小二乘加权数据融合算法已被广泛地应用于融合多传感器系统中的量测信息。但是,通过深入的理论分析和实验仿真发现,自学习最小二乘加权数据融合算法在进行双传感器数据融合时具有较差的融合精度,同时该算法还具有较差的抗干扰性及稳定性。基于以上研究结果,提出了一种基于全局状态估计的多传感器加权数据融合算法,采用卡尔曼滤波的状态估计特性及相关历史信息,使得状态的估计值能够充分逼近真实值,从而使得算法具有较高的融合精度及抗干扰性。最后,Monte Carlo仿真结果显示,相比于已有算法,提出的算法在融合精度及抗干扰性方面具有明显地提高。
The principle of least squares based on self-learning weighted data fusion algorithm （PLS-SWFA） has been widely used to fuse measured data in multi-sensor systems.However,via extensive analysis and evaluation,we find that PLS-SWFA has low data fusion accuracy in two-sensors systems and also has low anti-jamming ability.To address this problem,our paper proposes a multi-sensor weighted data fusion algorithm （GSE-MWFA） based on global state estimation.Based on the state estimation of kalman filter,GSE-MWFA can use the historical data to make the fused data fully approximate the real-data of target state.Through extensive theoretical analysis and experiments,our data show that GSE-MWFA achieves higher fusion accuracy and greater anti-jamming ability in comparison with the existing algorithms.
出处
《红外技术》
CSCD
北大核心
2014年第5期360-364,共5页
Infrared Technology
基金
中国空空导弹研究院科技创新基金
编号:201306S08
