面向空间定位系统的分布式融合估计

针对网络化高精度测量定位中的测量规模大、通信约束复杂以及处理信号协同困难等问题,基于线性CCD的空间定位原理,研究带有交叉相关噪声和传输时滞的不确定网络化系统的信息融合估计问题。提出了一种基于Kalman滤波的分布式感知和集中式融合的估计方法,应用测量转换策略,采用两层加权融合理论,设计了最优加权融合估计方法。减轻了处理传输延迟的通信负担和计算复杂度,提高了抵抗噪声干扰的鲁棒性能,进而减少信息冗余,并保持较高的测量精度。实验部分验证了该方法的有效性。

Distributed fusion estimation for spatial localization system

In the networked high precision measurement and localization, the requirements of growing measurement scale, enlarging communication constraints and increasing signal coordination processing bring great challenges in this domain. Based on the spatial localization principle, the information fusion estimation is investigated for uncertain networked systems with cross correlated noises and data transmission delays. A distributed fusion estimation scheme is proposed by distributed perception and centralized fusion based on Kalman filtering. The scheme designs an optimal weighted fusion estimator employing the measurement transformation and the two stage weighted fusion approaches. As a result, the communication burden and computational cost with network induced transmission delays can be alleviated, and the noisy disturbances can be decomposed, and robustness can be improved. Moreover, information redundancy can be reduce and the higher measurement accuracy can be maintained. An illustrative example is given to validate the effectiveness of the proposed method.