Particle filtering provides an approximate representation of a tracked posterior density which converges asymptotically to the true posterior as the number of particles used increases. The greater the number of particles, the higher the computational complexity. This complexity can be implemented by operating the particle filter in parallel architectures. However, the resampling step in the particle filter requires a high level of synchronization and extensive information interchange between the particles, which impedes the use of parallel hardware systems. This paper establishes a new perspective for understanding particle filtering — that particle filtering can be achieved by adopting the principles of information exchange within a network, the nodes of which are now the particles in the particle filter. We propose to connect particles via a minimally connected network and resample each locally. This strategy facilitates full information exchange among the particles, but with each particle communicating with only a small fixed set of other particles, thus leading to minimal communication overhead. The key benefit is that this approach facilitates the use of many particles for accurate posterior approximation and tracking accuracy.

中文翻译：

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重采样和网络理论

粒子滤波提供了跟踪的后验密度的近似表示，随着使用的粒子数量的增加，该密度渐近收敛到真实的后验。粒子数越多，计算复杂度越高。这种复杂性可以通过在并行架构中操作粒子滤波器来实现。然而，粒子滤波器中的重采样步骤需要粒子之间的高度同步和广泛的信息交换，这阻碍了并行硬件系统的使用。本文为理解粒子滤波建立了一个新的视角——粒子滤波可以通过采用网络内信息交换的原理来实现，网络的节点现在是粒子滤波中的粒子。我们建议通过最小连接网络连接粒子并在本地重新采样每个粒子。这种策略促进了粒子之间的完整信息交换，但每个粒子只与一小部分固定的其他粒子进行通信，从而导致最小的通信开销。主要好处是这种方法有助于使用许多粒子来实现精确的后验近似和跟踪精度。