论文标题
KD-EKF：基于卡尔曼分解的一致的合作定位估计器
KD-EKF: A Consistent Cooperative Localization Estimator Based on Kalman Decomposition
论文作者
论文摘要
在本文中，我们从系统分解的角度重新审视了基于EKF的合作定位（CL）的不一致问题。通过将标准EKF使用的线性化系统转换为其可观察的典型形式，可以分离系统的可观察到的不可观察的组件。因此，在卡尔曼可观察的规范形式的状态传播和测量雅各布人中明确分离了导致缩小不可观察子空间维度的因素。在这些见解的推动下，我们提出了一种新的CL算法，称为KD-EKF，旨在提高一致性。 KD-EKF算法背后的关键思想涉及在转化的坐标中执行状态估计，以消除卡尔曼可观察的规范形式中可观察性的影响因素。结果，KD-EKF算法确保了正确的可观察性属性和一致性。我们通过蒙特卡洛模拟和现实世界实验广泛验证了KD-EKF算法的有效性。结果表明，在准确性和一致性方面，KD-EKF优于最先进的算法。
In this paper, we revisit the inconsistency problem of EKF-based cooperative localization (CL) from the perspective of system decomposition. By transforming the linearized system used by the standard EKF into its Kalman observable canonical form, the observable and unobservable components of the system are separated. Consequently, the factors causing the dimension reduction of the unobservable subspace are explicitly isolated in the state propagation and measurement Jacobians of the Kalman observable canonical form. Motivated by these insights, we propose a new CL algorithm called KD-EKF which aims to enhance consistency. The key idea behind the KD-EKF algorithm involves perform state estimation in the transformed coordinates so as to eliminate the influencing factors of observability in the Kalman observable canonical form. As a result, the KD-EKF algorithm ensures correct observability properties and consistency. We extensively verify the effectiveness of the KD-EKF algorithm through both Monte Carlo simulations and real-world experiments. The results demonstrate that the KD-EKF outperforms state-of-the-art algorithms in terms of accuracy and consistency.
