In practical applications, the measurement noise statistics is usually unknown or may change over time. However, most existing distributed filtering algorithms for sensor networks are constructed based on exact knowledge of measurement noise statistics. Therefore, under situations with measurement uncertainty, the existing algorithms may result in deteriorated performance. To solve such problems, a distributed adaptive cubature information filter based on variational Bayesian (VB-DACIF) is proposed here. Firstly, the predicted estimates of interest from inclusive neighbours are fused by minimizing the weighted Kullback-Leibler average, in which the cubature rule is utilized to tackle system nonlinearity. Then, the free form variational Bayesian approximation is applied to recursively update both the local estimate and the precision matrices of sensing nodes. Finally, the posterior Cramér-Rao lower bound is exploited to evaluate performance of the proposed VB-DACIF. Simulation results with a maneuvering target tracking scenario validates the feasibility and superiority of the proposed VB-DACIF.

在实际应用中，测量噪声统计信息通常是未知的，或者会随时间变化。但是，大多数现有的传感器网络分布式滤波算法都是基于对测量噪声统计信息的确切了解而构建的。因此，在测量不确定的情况下，现有算法可能会导致性能下降。为了解决这些问题，在此提出了一种基于变分贝叶斯（VB-DACIF）的分布式自适应培养信息滤波器。首先，通过最小化加权的Kullback-Leibler平均数来融合来自包容性邻居的预期利益估计，其中使用了孵化规则来解决系统非线性问题。然后，应用自由形式变分贝叶斯逼近来递归更新感知节点的局部估计和精度矩阵。最后，利用后Cramér-Rao下界来评估所提出的VB-DACIF的性能。机动目标跟踪方案的仿真结果验证了所提出的VB-DACIF的可行性和优越性。