Distributed Kalman Filter algorithms for self-localization of mobile devices

2010 ◽  
Author(s):  
Anne-Kathrin Hess ◽  
Anders Rantzer
Keyword(s):  
Kalman Filter ◽  
Mobile Devices ◽  
Filter Algorithms

scholarly journals An Integrated Adaptive Kalman Filter for High-Speed UAVs

2019 ◽  
Vol 9 (9) ◽  
pp. 1916 ◽  
Author(s):  
Tiantian Huang ◽  
Hui Jiang ◽  
Zhuoyang Zou ◽  
Lingyun Ye ◽  
Kaichen Song
Keyword(s):  
Kalman Filter ◽  
High Speed ◽  
Sensor Data ◽  
Adaptive Kalman Filter ◽  
Fusion Algorithm ◽  
Multi Sensor Data Fusion ◽  
Aerial Vehicle ◽  
Robust Adaptive ◽  
Tracking Filter ◽  
Filter Algorithms

In order to solve the problems of filtering divergence and low accuracy in Kalman filter (KF) applications in a high-speed unmanned aerial vehicle (UAV), this paper proposed a new method of integrated robust adaptive Kalman filter: strong adaptive Kalman filter (SAKF). The simulation of two high-dynamic conditions and a practical experiment were designed to verify the new multi-sensor data fusion algorithm. Then the performance of the Sage–Husa adaptive Kalman filter (SHAKF), strong tracking filter (STF), H∞ filter and SAKF were compared. The results of the simulation and practical experiments show that the SAKF can automatically select its filtering process under different conditions, according to an anomaly criterion. SAKF combines the advantages of SHAKF, H∞ filter and STF, and has the characteristics of high accuracy, robustness and good tracking skill. The research has proved that SAKF is more appropriate in high-speed UAV navigation than single filter algorithms.

scholarly journals Diffusion Strategies for Distributed Kalman Filter with Dynamic Topologies in Virtualized Sensor Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Shujie Yang ◽  
Tao Huang ◽  
Jianfeng Guan ◽  
Yongping Xiong ◽  
Mu Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Kalman Filter ◽  
Sensor Networks ◽  
Network Virtualization ◽  
Physical Layer ◽  
Wireless Sensor ◽  
Kalman Filter Algorithm ◽  
The Mean ◽  
Improved Performance ◽  
Filter Algorithms

Network virtualization has become pervasive and is used in many applications. Through the combination of network virtualization and wireless sensor networks, it can greatly improve the multiple applications of traditional wireless sensor networks. However, because of the dynamic reconfiguration of topologies in the physical layer of virtualized sensor networks (VSNs), it requires a mechanism to guarantee the accuracy of estimate values by sensors. In this paper, we focus on the distributed Kalman filter algorithm with dynamic topologies to support this requirement. As one strategy of distributed Kalman filter algorithms, diffusion Kalman filter algorithm has a better performance on the state estimation. However, the existing diffusion Kalman filter algorithms all focus on the fixed topologies. Considering the dynamic topologies in the physical layer of VSNs mentioned above, we present a diffusion Kalman filter algorithm with dynamic topologies (DKFdt). Then, we emphatically derive the theoretical expressions of the mean and mean-square performance. From the expressions, the feasibility of the algorithm is verified. Finally, simulations confirm that the proposed algorithm achieves a greatly improved performance as compared with a noncooperative manner.

Initial Alignment of Shipborne SINS under Ship Motion

2020 ◽  
Vol 28 (3) ◽  
pp. 3-17 ◽  
Author(s):  
G.I. Emel’yantsev ◽  
◽  
A.P. Stepanov ◽  
B.A. Blazhnov ◽  
◽  
...  
Keyword(s):  
Kalman Filter ◽  
Ship Motion ◽  
Second Step ◽  
Alignment Algorithm ◽  
Initial Alignment ◽  
Log Data ◽  
Current Attitude ◽  
Alignment Errors ◽  
Filter Algorithms ◽  
Water Speed

The paper focuses on improving the accuracy and shortening the time of shipborne SINS initial alignment under the ship yaw, roll and pitch. This is achieved by implementing a two-step SINS alignment algorithm. At the first step, the ship current attitude parameters are approximately autonomously estimated by data from gyros and accelerometers with account for its dynamics and using water speed log data. At the second step, the system fine alignment is performed with account for alignment errors after the completion of the first step. Speed and position measurements from external aids are additionally applied during the fine alignment. Kalman filter algorithms are used in the first and second steps. Results from bench and sea tests for SINS on navigation grade FOGs under the ship yaw, roll and pitch motion are provided.

scholarly journals A Unification of Ensemble Square Root Kalman Filters

2012 ◽  
Vol 140 (7) ◽  
pp. 2335-2345 ◽  
Author(s):  
Lars Nerger ◽  
Tijana Janjić ◽  
Jens Schröter ◽  
Wolfgang Hiller
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Kalman Filters ◽  
Numerical Experiments ◽  
Computational Cost ◽  
Square Root ◽  
Symmetric Square ◽  
Ensemble Transform Kalman Filter ◽  
Filter Algorithms ◽  
Ensemble Transform

Abstract In recent years, several ensemble-based Kalman filter algorithms have been developed that have been classified as ensemble square root Kalman filters. Parallel to this development, the singular “evolutive” interpolated Kalman (SEIK) filter has been introduced and applied in several studies. Some publications note that the SEIK filter is an ensemble Kalman filter or even an ensemble square root Kalman filter. This study examines the relation of the SEIK filter to ensemble square root filters in detail. It shows that the SEIK filter is indeed an ensemble square root Kalman filter. Furthermore, a variant of the SEIK filter, the error subspace transform Kalman filter (ESTKF), is presented that results in identical ensemble transformations to those of the ensemble transform Kalman filter (ETKF), while having a slightly lower computational cost. Numerical experiments are conducted to compare the performance of three filters (SEIK, ETKF, and ESTKF) using deterministic and random ensemble transformations. The results show better performance for the ETKF and ESTKF methods over the SEIK filter as long as this filter is not applied with a symmetric square root. The findings unify the separate developments that have been performed for the SEIK filter and the other ensemble square root Kalman filters.

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