中文摘要：

针对同时具有量测时滞和相关噪声干扰的多传感器系统,设计了一种低维的集中式融合估计方法。首先通过迭代正交变换的方法解除系统量测噪声之间的相关性,得到系统的＂伪量测＂模型,同时用一个独立同分布的伯努利变量对数据传输的延时过程进行建模。在此基础上,利用一定长度的存储器对到达融合中心的数据包进行重新排序,并结合卡尔曼滤波和信息滤波给出一种低维的集中式融合估计算法,最后分析了所提算法的稳定性。仿真实验证明了算法的有效性。

英文摘要：

An optimal lower-dimensional centralized data fusion estimation method is designed for the multi-sensor system with delayed measurements and correlated noises. First,the recursive orthogonal transformation method is applied to remove the noise correlation and an independent identically distributed random variable is used to model the time delay process. With the buffer of a certain length,a new lower-dimensional centralized fusion algorithm is then proposed by combining the Kalman filter and information filter, and the stability of the system is analyzed. The simulation results show the effectiveness of the proposed algorithms.