This paper addresses the challenges posed by imperfect detection and uncertain parameters, such as detection probability and noise covariances, in target tracking. We introduce an Adaptive Bernoulli Filter (ABF) capable of handling multiple sources of uncertainty simultaneously. The ABF employs a Gaussian Inverse Gamma Inverse Wishart Mixture (GIGIWM) to represent the spatial probability density function of the augmented state. Using a variational Bayesian approach, we derive a closed-form solution for the filter, providing estimates for target existence probability, kinematic and feature states, measurement noise covariance matrix, and predicted error covariance matrix. Additionally, we extend the ABF to incorporate prior knowledge through constrained distributions. In a distributed multi-sensor scenario, we propose a fusion approach to combine local posteriors, extending existing fusion techniques to handle local posteriors that depend on both global and local variables. Simulation results show the effectiveness and robustness of the proposed filter and distributed fusion framework.

中文翻译：

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不确定性下多传感器目标跟踪的分布式自适应伯努利滤波


本文解决了目标跟踪中不完善的检测和不确定参数（例如检测概率和噪声协方差）带来的挑战。我们引入了自适应伯努利滤波器（ABF），能够同时处理多个不确定性来源。 ABF 采用高斯逆伽玛逆威沙特混合 (GIGIWM) 来表示增广态的空间概率密度函数。使用变分贝叶斯方法，我们得出滤波器的封闭式解，提供目标存在概率、运动学和特征状态、测量噪声协方差矩阵和预测误差协方差矩阵的估计。此外，我们还扩展了 ABF，通过约束分布合并先验知识。在分布式多传感器场景中，我们提出了一种融合局部后验的融合方法，扩展现有的融合技术来处理依赖于全局和局部变量的局部后验。仿真结果表明了所提出的滤波器和分布式融合框架的有效性和鲁棒性。