Learning Flock：利用神经增强技术提升用于多子状态粒子滤波的粒子集合
Learning Flock: Enhancing Sets of Particles for Multi~Sub-State Particle Filtering with Neural Augmentation
AI总结：
本文提出一种名为 Learning Flock 的深度神经网络增强方法，通过学习纠正粒子-权重集合关系，在减少粒子数量的同时保持准确性，显著提升了多子状态粒子滤波在多目标跟踪中的性能、鲁棒性与延迟。
摘要：
用于具有多个子状态的动态系统状态估计的一类领先算法基于粒子滤波器（PFs）。当在复杂或近似建模（需要大量粒子）且具有低延迟要求（限制粒子数量）的条件下运行时，PFs 通常会遇到困难，多目标跟踪（MTT）通常就是这种情况。在这项工作中，我们引入了一种针对 PFs 的深度神经网络（DNN）增强方法，称为 learning flock (LF)。LF 学习纠正粒子-权重集合（我们称之为 flock），其基于集合本身内所有子粒子之间的关系，而忽略集合获取过程。我们提出的 LF 可以轻松整合到不同的 PFs 流程中，旨在通过在减少粒子数量的情况下保持准确性来促进快速操作。我们引入了一种专门的训练算法，支持有监督和无监督训练，并生成一个支持不同数量子状态和粒子的模块，而无需重新训练。我们通过实验展示了 LF 增强在雷达多目标跟踪中的性能、鲁棒性和延迟方面的改进，以及其缓解不匹配观测建模影响的能力。我们还比较并展示了 LF 相对于最先进的 DNN 辅助 PF 的优势，并证明 LF 既增强了经典 PFs，也增强了基于 DNN 的滤波器。
英文摘要：
A leading family of algorithms for state estimation in dynamic systems with multiple sub-states is based on particle filters (PFs). PFs often struggle when operating under complex or approximated modelling (necessitating many particles) with low latency requirements (limiting the number of particles), as is typically the case in multi target tracking (MTT). In this work, we introduce a deep neural network (DNN) augmentation for PFs termed learning flock (LF). LF learns to correct a particles-weights set, which we coin flock, based on the relationships between all sub-particles in the set itself, while disregarding the set acquisition procedure. Our proposed LF, which can be readily incorporated into different PFs flow, is designed to facilitate rapid operation by maintaining accuracy with a reduced number of particles. We introduce a dedicated training algorithm, allowing both supervised and unsupervised training, and yielding a module that supports a varying number of sub-states and particles without necessitating re-training. We experimentally show the improvements in performance, robustness, and latency of LF augmentation for radar multi-target tracking, as well its ability to mitigate the effect of a mismatched observation modelling. We also compare and illustrate the advantages of LF over a state-of-the-art DNN-aided PF, and demonstrate that LF enhances both classic PFs as well as DNN-based filters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
