多传感器系统中数据包身份错乱下的融合估计
Fusion Estimation in Multi-sensor Systems for Data Packets with Disrupted Identities
AI总结：
针对多传感器系统中数据包身份错乱导致的融合估计性能下降问题，提出基于排列与对称群的建模方法，并开发贝叶斯与贪心两种算法，均保证期望误差有界，且通过期望最大化算法处理信息稀缺场景。
AI中文摘要：
本文探讨了多传感器系统中的融合估计问题，其中每个传感器接收到的数据包的身份可能因设备身份分配混乱、通信协议缺陷或缺乏明确的传感器标识符而受到干扰或出错。这可能导致融合估计过程中数据分量被随机打乱，从而损害融合估计的性能。为解决此问题，我们引入排列和对称群的概念，将这一现象描述为数据包排列。我们构造统计量以简化信息集，并开发了两种算法：贝叶斯方法，利用后验排列概率进行融合；以及贪心方法，通过猜测可能的数据排列有效提升估计性能。我们比较了这两种算法，并证明两者均具有期望误差有界性。我们针对信息稀缺场景改进了算法。通过采用期望最大化算法，我们填补了数据排列的先验信息，并证明了其正确收敛。最后，我们通过数值模拟验证了我们的结果。
英文摘要：
In this paper, we explore the problem of fusion estimation for a multi-sensor system where the identity of the data packet received by each sensor may be disrupted or incorrect due to confusion in device identity allocation, communication protocol defects, or the lack of a clear sensor identifier. This can result in a random shuffle of the data components during the fusion estimation process, compromising the performance of the fusion estimation. To address this issue, we introduce the concepts of permutations and symmetry groups to describe this phenomenon as data packet permutation. We construct statistics to simplify the information set, developing two algorithms: a Bayesian approach, which performs fusion using posterior arrangement probabilities, and a greedy approach, which effectively improves estimation performance by guessing the likely data arrangement. We compare these two algorithms and demonstrate that both are expectation error-bounded. We improve algorithms for information-scarce scenarios. By employing the expectation-maximization algorithm, we fill in the prior information of data arrangement where the correct convergence is proven. Finally, we present numerical simulations to validate our results.
