We consider the distributed Kalman filtering (DKF) with non-Gaussian noises problem, where each sensor exchanges information between its neighbors with limited communication. Inspired by the ability to capture higher-order statistics of maximum correntropy criterion (MCC) to deal with non-Gaussian noises, we utilizes a matrix weight instead of a scalar obtained by MCC to improve the estimation performance comparing with existing MCC based DKFs. We approximate the centralized estimate by the covariance intersection method, and propose a new MCC based distributed Kalman filter, named CI-DMCKF. The proposed algorithm only needs to communicate once with neighbors in a sampling period, which is more efficient for low bandwidth communication than existing MCC based DKFs. Under the condition of global observability, we show that the consistency, stability, and asymptotic unbiasedness properties of proposed CI-DMCKF algorithm. Finally, we experimentally demonstrate the effectiveness of the proposed algorithm on a cooperating target tracking task.

中文翻译：

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传感器网络上具有最大熵准则的高效分布式卡尔曼滤波器


我们考虑具有非高斯噪声问题的分布式卡尔曼滤波（DKF），其中每个传感器通过有限的通信在其邻居之间交换信息。受到捕获最大相关熵准则（MCC）的高阶统计量来处理非高斯噪声的能力的启发，我们利用矩阵权重代替 MCC 获得的标量来提高估计性能，与现有的基于 MCC 的 DKF 相比。我们通过协方差交集法来近似集中估计，并提出了一种新的基于MCC的分布式卡尔曼滤波器，命名为CI-DMCKF。所提出的算法只需要在一个采样周期内与邻居通信一次，这对于低带宽通信比现有的基于 MCC 的 DKF 更有效。在全局可观测的条件下，我们证明了所提出的CI-DMCKF算法的一致性、稳定性和渐近无偏性。最后，我们通过实验证明了所提出的算法在协作目标跟踪任务上的有效性。