Building on the classical interacting multiple model algorithm, this article develops a novel distributed approach for solving the problem of distributed state estimation for stochastic linear hybrid systems. In our distributed framework, a network of sensors is employed and each of them measures only a portion of the system outputs, thus, the system may not necessarily be observable for any individual sensor. To tackle with this, we develop an effective data fusion scheme that enables the sensor network to collectively measure the output of the hybrid system, by leveraging the communication between neighboring sensors. Consequently, each sensor only needs to process a relatively small set of data and is able to locally and identically observe the states (both the continuous states and discrete modes) of stochastic linear hybrid systems. Stability of the proposed distributed algorithm is proved theoretically, in the sense that the covariance of estimation error is lower and upper bounded. Finally, the performance of the proposed algorithm is demonstrated via illustrative simulations on a maneuvering aircraft tracking problem.

中文翻译：

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具有有限时间融合的随机线性混合系统的分布式状态估计

本文基于经典的交互多模型算法，开发了一种新颖的分布式方法，用于解决随机线性混合系统的分布式状态估计问题。在我们的分布式框架中，使用了一个传感器网络，每个传感器只测量系统输出的一部分，因此，对于任何单个传感器，系统不一定是可观察的。为了解决这个问题，我们开发了一种有效的数据融合方案，通过利用相邻传感器之间的通信，使传感器网络能够共同测量混合系统的输出。因此，每个传感器只需要处理相对较小的数据集，并且能够在本地和相同地观察随机线性混合系统的状态（连续状态和离散模式）。从理论上证明了所提出的分布式算法的稳定性，即估计误差的协方差具有下界和上界。最后，通过对机动飞机跟踪问题的说明性模拟证明了所提出算法的性能。