基于消息传递的9维协同定位与导航及嵌入式粒子流
Message Passing-Based 9-D Cooperative Localization and Navigation with Embedded Particle Flow
AI总结：
本文提出一种结合粒子流与置信传播的分布式9维协同定位导航算法，以少量粒子避免粒子退化，并在精度和运行时间上优于传统粒子BP及集中式方法。
摘要：
协同定位（CL）是面向位置感知通信网络、现代便利设施和公共安全等创新服务的一项重要技术。我们考虑由移动智能体组成的无线网络，这些智能体旨在通过彼此之间进行成对测量并交换各自的位置信息来实现自身定位。置信传播（BP）是一种用于协同定位的最先进贝叶斯方法。在协同定位中，通常采用基于粒子的BP实现，以应对非线性测量模型和状态动态。然而，已知基于粒子的BP算法在具有高维状态的大规模密集移动智能体网络中会遭受粒子退化问题。本文利用粒子流推导协同定位中BP的消息，从而开发出一种分布式基于粒子的消息传递算法，该算法避免了粒子退化。我们提出的基于粒子流的组合BP方法能够以最少数量的粒子为智能体状态计算高精度的建议分布。它在精度和运行时间方面优于传统的基于粒子的BP算法。此外，我们将所提出的方法与一种集中式基于粒子流的实现（即精确的Daum-Huang滤波器）以及sigma点BP在位置精度、运行时间和内存需求随网络规模变化方面进行了比较。我们进一步将所有方法与后验克拉美-罗下界（PCRLB）提供的理论性能极限进行对比。基于三个不同场景，我们证明了所提出方法的优越性。
英文摘要：
Cooperative localization (CL) is an important technology for innovative services such as location-aware communication networks, modern convenience, and public safety. We consider wireless networks with mobile agents that aim to localize themselves by performing pairwise measurements amongst agents and exchanging their location information. Belief propagation (BP) is a state-of-the-art Bayesian method for CL. In CL, particle-based implementations of BP often are employed that can cope with non-linear measurement models and state dynamics. However, particle-based BP algorithms are known to suffer from particle degeneracy in large and dense networks of mobile agents with high-dimensional states. This paper derives the messages of BP for CL by means of particle flow, leading to the development of a distributed particle-based message-passing algorithm which avoids particle degeneracy. Our combined particle flow-based BP approach allows the calculation of highly accurate proposal distributions for agent states with a minimal number of particles. It outperforms conventional particle-based BP algorithms in terms of accuracy and runtime. Furthermore, we compare the proposed method to a centralized particle flow-based implementation, known as the exact Daum-Huang filter, and to sigma point BP in terms of position accuracy, runtime, and memory requirement versus the network size. We further contrast all methods to the theoretical performance limit provided by the posterior Cramér-Rao lower bound (PCRLB). Based on three different scenarios, we demonstrate the superiority of the proposed method.
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