关键词: 最小二乘, 多传感器数据融合, 数据融合

Abstract: For the sake of effect fusion of multi-sensor redundant system metrical information，making the value of state estimation approach to the true value and retaliating high accuracy and high reliability state estimation，three algorithms，based on optimally weighted least square method（OW-LSM）and finite windowing weighted algorithm（FW-LSM）and the self-learning weighted least squares（SL-LSM），are applied to information fusion of multi-sensor data respectively.The mean variance of data is reduced，estimation accuracy is advanced dramatically.Then the simulation comparison with traditional least squares is performed.Results show that the three algorithms has higher accuracy than the traditional one.Therein，Self-learning weighted least squares taken the effect of history data and ambient noise and new sample value into account，enhanced sensitivity to noise measure，has better estimation effect.

Key words: least square method, multi-sensor data fusion, data fusion