Shan Xiaoyu, Cabani Adnane, Chafouk Houcine
ESIGELEC, IRSEEM, Université de Rouen Normandie, 76000 Rouen, France.
Sensors (Basel). 2024 May 14;24(10):3124. doi: 10.3390/s24103124.
The data incest problem causes inter-estimate correlation during data fusion processes, which yields inconsistent data fusion results. Especially in the multi-sensor multi-vehicle (MSMV) system, the data incest problem is serious due to multiple relative position estimations, which not only lead to pessimistic estimation but also cause additional computational overhead. In order to address the data incest problem, we propose a new data fusion method termed the interval split covariance intersection filter (ISCIF). The general consistency of the ISCIF is proven, serving as supplementary proof for the split covariance intersection filter (SCIF). Moreover, a decentralized MSMV localization system including absolute and relative positioning stages is designed. In the absolute positioning stage, each vehicle uses the ISCIF algorithm to update its own position based on absolute measurements. In the relative position stage, the interval constraint propagation (ICP) method is implemented to preprocess multiple relative position estimates and initially prepare input data for ISCIF. Then, the proposed ISCIF algorithm is employed to realize relative positioning. In addition, comparative simulations demonstrate that the proposed method can achieve both accurate and consistent results compared with the state-of-the-art methods.
数据乱伦问题在数据融合过程中会导致估计值之间的相关性，从而产生不一致的数据融合结果。特别是在多传感器多车辆（MSMV）系统中，由于多个相对位置估计，数据乱伦问题很严重，这不仅会导致悲观估计，还会造成额外的计算开销。为了解决数据乱伦问题，我们提出了一种新的数据融合方法，称为区间分裂协方差交叉滤波器（ISCIF）。证明了ISCIF的一般一致性，为分裂协方差交叉滤波器（SCIF）提供了补充证明。此外，设计了一个包括绝对定位和相对定位阶段的分布式MSMV定位系统。在绝对定位阶段，每辆车使用ISCIF算法根据绝对测量值更新自己的位置。在相对定位阶段，采用区间约束传播（ICP）方法对多个相对位置估计进行预处理，并为ISCIF初步准备输入数据。然后，使用所提出的ISCIF算法实现相对定位。此外，对比仿真表明，与现有方法相比，所提方法能够实现准确且一致的结果。
