Collaborative Multiple UAVs Navigation With GPS/INS/UWB Jammers Using Sigma Point Belief Propagation
نویسندگان
چکیده
منابع مشابه
Target Localization using Multiple UAVs with Sensor Fusion via Sigma-Point Kalman Filtering
This paper proposes a sensor-fusion methodology based on sigma-point Kalman filtering (SPKF) techniques, where the SPKF is applied to the particular problem of localizing a mobile target using uncertain nonlinear measurements relating to target positions. This SPKF-based sensor fusion is part of our larger research program in which the particular target localization solution of interest must ad...
متن کاملRobust all-source positioning of UAVs based on belief propagation
For unmanned air vehicles (UAVs) to survive hostile operational environments, it is always preferable to utilize all wireless positioning sources available to fuse a robust position. While belief propagation is a well-established method for all source data fusion, it is not an easy job to handle all the mathematics therein. In this work, a comprehensive mathematical framework for belief propaga...
متن کاملSigma-Point Kalman Filters for Integrated Navigation
Core to integrated navigation systems is the concept of fusing noisy observations from GPS, Inertial Measurement Units (IMU), and other available sensors. The current industry standard and most widely used algorithm for this purpose is the extended Kalman filter (EKF) [6]. The EKF combines the sensor measurements with predictions coming from a model of vehicle motion (either dynamic or kinemati...
متن کاملFixed Point Solutions of Belief Propagation
Belief propagation (BP) is an iterative method to perform approximate inference on arbitrary graphical models. Whether BP converges and if the solution is a unique fixed point depends on both, the structure and the parametrization of the model. To understand this dependence it is interesting to find all fixed points. In this work, we formulate a set of polynomial equations, the solutions of whi...
متن کاملSubgraph Detection with cues using Belief Propagation
We consider an Erdős-Rényi graph with n nodes and edge probability q that is embedded with a random subgraph of size K with edge probabilities p such that p > q. We address the problem of detecting the subgraph nodes when only the graph edges are observed, along with some extra knowledge of a small fraction of subgraph nodes, called cued vertices or cues. We employ a local and distributed algor...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3031605