This paper investigates the particle filtering problem for a class of nonlinear/non-Gaussian systems under the dynamic event-triggered protocol. In order to avert frequent data transmission and reduce the communication overhead, a dynamic event-triggered transmission mechanism is adopted to decide whether the data should be transmitted or not. We first consider a scenario where all sensor nodes selectively transmit their newly obtained measurements to a central node, and a full likelihood function at the central node is derived by fusing the transmitted measurements and the information embodied in the non-triggered measurements. Based on the derived full likelihood function, a centralized auxiliary particle filtering algorithm is proposed to select those particles that are more likely to match the current measurement information. Next, based on the diffusion strategy, a distributed auxiliary particle filtering algorithm is further developed, where the local measurements and the local posteriors (approximated by the Gaussian mixture models) are exchanged among neighboring nodes under the dynamic event-triggered communication strategy. Finally, the effectiveness of the proposed filtering schemes is demonstrated via Monte Carlo simulations in a target tracking problem with received-signal-strength sensors.

中文翻译：

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用于目标跟踪的具有扩散策略的分布式辅助粒子过滤：一种动态事件触发方法

本文研究了动态事件触发协议下一类非线性/非高斯系统的粒子滤波问题。为了避免频繁的数据传输，减少通信开销，采用动态事件触发传输机制来决定数据是否应该传输。我们首先考虑一种场景，其中所有传感器节点选择性地将其新获得的测量值传输到中央节点，并且通过将传输的测量值与包含在非触发测量值中的信息融合，导出中央节点的全似然函数。基于导出的全似然函数，提出集中式辅助粒子滤波算法，以选择那些更可能匹配当前测量信息的粒子。下一个，在扩散策略的基础上，进一步开发了一种分布式辅助粒子滤波算法，其中在动态事件触发通信策略下，在相邻节点之间交换局部测量和局部后验（由高斯混合模型近似）。最后，在使用接收信号强度传感器的目标跟踪问题中，通过蒙特卡罗模拟证明了所提出的滤波方案的有效性。