考虑相关噪声与网络延迟的多传感器数据融合（英文）

Multi-sensor Data Fusion with Network Delays and Correlated Noises

针对多传感器系统中存在的相关噪声以及网络延迟问题,提出了一种带有缓存器的序贯式融合滤波算法。利用正交迭代法解除了噪声之间的相关性。针对不可靠网络导致的延迟甚至丢包问题,引入缓存器存储量测值,并在缓存器中利用时间戳对量测值进行重新排序。在此基础上提出了一种新型低维序贯式融合滤波算法,相比于传统的序贯融合算法具有更好的估计性能,同时能有效降低噪声相关和网络延时对系统性能造成的影响。最后仿真验证了该算法的有效性。

This paper focuses on the state estimation for multi-sensor system with network delays and correlated noises. An orthogonal transformation method is applied to remove the correlations between different noises. For the problem of packet delays due to the unreliable network, a buffer with certain length is introduced to store the measurements, and the measurements are reordered using a timestamp in the buffer. Based on that, a novel sequential data fusion algorithm is proposed, by which the influence of the noise correlation and packet delays can be weakened effectively. Compared with the traditional sequential fusion method, the proposed algorithm has higher estimation accuracy. Simulation results show the effectiveness of the proposed algorithms.