We propose a divide-and-conquer approach to filtering which decomposes the state variable into low-dimensional components to which standard particle filtering tools can be successfully applied and recursively merges them to recover the full filtering distribution. It is less dependent upon factorization of transition densities and observation likelihoods than competing approaches and can be applied to a broader class of models. Performance is compared with state-of-the-art methods on a benchmark problem and it is demonstrated that the proposed method is broadly comparable in settings in which those methods are applicable, and that it can be applied in settings in which they cannot.

翻译：我们建议一种分而治之的过滤方法,将状态变量分解成低维组成部分,标准粒子过滤工具可以成功应用,并循环合并,以回收全部过滤分布。它比相竞方法更不取决于过渡密度和观察可能性的因子化,可以适用于更广泛的模型类别。 业绩与基准问题的最新方法进行比较,并证明,在适用这些方法的环境中,拟议方法具有广泛的可比性,并且可以适用于无法应用这些方法的环境。