SARSA in extended Kalman Filter for complex urban environments positioning

TL;DR: To mitigate adverse effects from abnormal data during transmissions, in the constructed local filters, a mechanism is proposed which utilizes a saturation function to constrain the propagated innovations within a dynamically changeable bound.

TL;DR: This paper proposes a Multi-Normal-Inverse Wishart mixture distribution-based nonlinear filter, VB-EKF, to address inaccurate noise covariance matrices and measurement bias in Kalman filter-based spacecraft navigation, improving accuracy and robustness in complex environments.

TL;DR: An adaptive Kalman filtering algorithm based on maximum likelihood estimation is proposed, which determines the window size and window weight size by designing a window adaptive selection function and a weight function to change the innovation covariance at the kth moment so that the measurement noise covariance can better adapt to the changes in the environment.

Abstract: Nonlinear filtering algorithms have significant implications in the optimal estimation of navigation states and in improving the accuracy, reliability, and robustness of navigation systems. This manuscript surveys the developments of the nonlinear filtering algorithms (extended Kalman filtering (EKF), unscented Kalman filtering (UKF), Cubature Kalman filtering (CKF), particle filtering (PF), neural network filtering (NNF)) and adaptive/robust KF in integrated navigation systems. The principle, application, and existing problems of these nonlinear filtering algorithms are mainly studied, and the comparative analysis and prospect are carried out.

TL;DR: In this paper, a review of the state-of-the-art results for secure state estimation and control of CPSs is provided, in light of different performance indicators and defense strategies.

TL;DR: The basic performance of BDS-3 is described and some methods to improve the positioning, navigation and timing (PNT) service are suggested and the comprehensive and resilient PNT infrastructures are proposed for the future seamless PNT services.

TL;DR: Experimental evaluation of the proposed improved Q- learning under the challenging environment with a different layout of obstacles shows that the convergence of Q-learning can be accelerated when Q-values are initialized appropriately using the FPA.