中文摘要：

针对带相关观测噪声和带不同未知观测函数的多传感器离散系统,在已有的融合算法基础上提出了基于Bayes估计的加权最小二乘（Bayes estimation weighted least squares,BYEWLS）分布式融合Kalman滤波算法。该方法充分利用未知参数的验前信息,以风险函数为评价指标,证明了BYEWLS融合算法优于WLS融合算法,针对BYEWLS融合算法是有偏估计,提出了在线消除偏差的方法。分布式融合算法减少了计算负担,提高了融合精度,便于实时应用。最后通过仿真例子验证了该方法的有效性和理论分析的正确性。

英文摘要：

Under the multi-sensor discrete system with correlated measurement noises and different unknown measurement functions,the distributed fusion Kalman filtering algorithm is proposed according to the existing fusion algorithms based on Bayes estimation weighted least squares（BYEWLS）.The algorithm makes full use of the prior information for the unknown parameters.BYEWLS algorithm gains an advantage over WLS algorithm according to risk function.Meanwhile,the on-line method is proposed to eliminate the bias emerged with BYEWLS fusion algorithm.The distributed fusion algorithms can reduce the computational burden and improve the fusion accuracy,therefore they are suitable for real-time applications.As a result,the simulation example indicates the validity of the theory analysis.