In recent years, multisensor fusion for state estimation has gained considerable attention. The effectiveness of the optimal fusion estimation method heavily relies on the correlation among sensor measurement noises. To enhance estimate fusion performance by mining unknown correlation in the data, this paper introduces a novel multisensor fusion approach using an information filtering neural network (IFNet) for discrete-time nonlinear state space models with cross-correlated measurement noises. The method presents three notable advantages: First, it offers a data-driven perspective to tackle uncertain correlation in multisensor estimate fusion while preserving the interpretability of the information filtering. Second, by harnessing the RNN’s capability to manage data streams, it can dynamically update the fusion weights between sensors to improve fusion accuracy. Third, this method has a lower complexity than the state-of-the-art KalmanNet measurement fusion method when dealing with the fusion problem involving a large number of sensors. Numerical simulations demonstrate that IFNet exhibits better fusion accuracy than traditional filtering methods and KalmanNet fusion filtering when correlation among measurement noises is unknown.

中文翻译：

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IFNet：传感器测量噪声中具有未知相关性的数据驱动的多传感器估计融合


近年来，用于状态估计的多传感器融合受到了相当大的关注。最优融合估计方法的有效性在很大程度上依赖于传感器测量噪声之间的相关性。为了通过挖掘数据中的未知相关性来提高估计融合性能，本文引入了一种新的多传感器融合方法，该方法使用信息过滤神经网络 （IFNet） 对具有互相关测量噪声的离散时间非线性状态空间模型。该方法具有三个显著优势：首先，它提供了一种数据驱动的视角来解决多传感器估计融合中的不确定性相关性，同时保留了信息过滤的可解释性。其次，通过利用 RNN 管理数据流的能力，它可以动态更新传感器之间的融合权重，从而提高融合精度。第三，在处理涉及大量传感器的融合问题时，该方法的复杂度低于最先进的 KalmanNet 测量融合方法。数值仿真表明，当测量噪声之间的相关性未知时，IFNet 表现出比传统滤波方法和 KalmanNet 融合滤波更好的融合精度。