A dimensionless relative trajectory estimation algorithm for autonomous imaging of a small astronomical body in a close distance flyby

2016 ◽  
Vol 58 (4) ◽  
pp. 528-540 ◽  
Author(s):  
Kaito Ariu ◽  
Takaya Inamori ◽  
Ryu Funase ◽  
Shinichi Nakasuka
Keyword(s):  
Estimation Algorithm ◽  
Trajectory Estimation ◽  
Close Distance ◽  
Relative Trajectory

Relative Trajectory Estimation During Chang'e-2 Probe's Flyby of Asteroid Toutatis Using Dynamics, Optical, and Radio Constraints

2016 ◽  
Vol 54 (8) ◽  
pp. 4680-4693 ◽  
Author(s):  
Yanlong Bu ◽  
Wenlin Tang ◽  
Wenzhe Fa ◽  
Chibiao Ding ◽  
Geshi Tang ◽  
...  
Keyword(s):  
Trajectory Estimation ◽  
Relative Trajectory

Robust high-degree cubature information filter and its application to trajectory estimation for ballistic missile

2017 ◽  
Vol 232 (12) ◽  
pp. 2364-2377 ◽  
Author(s):  
Xiaogang Wang ◽  
Wutao Qin ◽  
Naigang Cui ◽  
Yu Wang
Keyword(s):  
State Estimation ◽  
Estimation Algorithm ◽  
Ballistic Missile ◽  
Measurement Noise ◽  
Estimation Accuracy ◽  
Trajectory Estimation ◽  
Information Filter ◽  
Non Gaussian ◽  
Time Update ◽  
High Degree

This paper presents a new recursive filter algorithm, the robust high-degree cubature information filter, which can provide reliable state estimation in the presence of non-Gaussian measurement noise. The novel algorithm is developed in the framework of the conventional information filter. The fifth-degree Cubature rule is utilized to improve the estimation accuracy and numerical stability during the time update, while the Huber technique is adopted in the measurements update stage. As the Huber technique is a combined minimum l1 and l2 norm estimation algorithm, the proposed algorithm could exhibit robustness to the non-Gaussian measurement noise, especially the glint noise. In addition, Monte Carlo simulation and the trajectory estimation for ballistic missile experiments demonstrate that the robust high-degree cubature information filter can provide improved state estimation performance over extended information filter and high-degree cubature information filter.

scholarly journals A generalized muon trajectory estimation algorithm with energy loss for application to muon tomography

2018 ◽  
Vol 123 (12) ◽  
pp. 124903 ◽  
Author(s):  
Stylianos Chatzidakis ◽  
Zhengzhi Liu ◽  
Jason P. Hayward ◽  
John M. Scaglione
Keyword(s):  
Energy Loss ◽  
Estimation Algorithm ◽  
Trajectory Estimation ◽  
Muon Tomography

Trajectory estimation algorithm for mobile nodes using encounter information and geographical information

2012 ◽  
Vol 8 (2) ◽  
pp. 249-270 ◽  
Author(s):  
Sae Fujii ◽  
Akira Uchiyama ◽  
Takaaki Umedu ◽  
Hirozumi Yamaguchi ◽  
Teruo Higashino
Keyword(s):  
Estimation Algorithm ◽  
Geographical Information ◽  
Mobile Nodes ◽  
Trajectory Estimation

scholarly journals Ultra - Wideband - based Trajectory Estimation Algorithm with Adaptive Square Root Cubature Kalman Filtering

2016 ◽  
Vol 2016 (3) ◽  
pp. 140-147
Author(s):  
Wu QingYi ◽  
◽  
Liu Zhong ◽  
Zhang JianQiang ◽  
Sun KaiWen ◽  
...  
Keyword(s):  
Kalman Filtering ◽  
Estimation Algorithm ◽  
Ultra Wideband ◽  
Square Root ◽  
Trajectory Estimation

scholarly journals CHARACTERIZATION OF A MOBILE MAPPING SYSTEM FOR SEAMLESS NAVIGATION

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 227-234 ◽  
Author(s):  
V. Di Pietra ◽  
N. Grasso ◽  
M. Piras ◽  
P. Dabove
Keyword(s):  
Autonomous Navigation ◽  
Estimation Algorithm ◽  
Mobile Mapping ◽  
Trajectory Estimation ◽  
Sensor Platform ◽  
Mapping System ◽  
Global Consistency ◽  
Mobile Mapping System ◽  
Correct Weight

Abstract. Mobile Mapping Systems (MMS) are multi-sensor technologies based on SLAM procedure, which provides accurate 3D measurement and mapping of the environment as also trajectory estimation for autonomous navigation. The major limits of these algorithms are the navigation and mapping inconsistence over the time and the georeferencing of the products. These issues are particularly relevant for pose estimation regardless the environment like in seamless navigation. This paper is a preliminary analysis on a proposed multi-sensor platform integrated for indoor/outdoor seamless positioning system. In particular the work is devoted to analyze the performances of the MMS in term of positioning accuracy and to evaluate its improvement with the integration of GNSS and UWB technology. The results show that, if the GNSS and UWB signal are not degraded, using the correct weight to their observations in the Stencil estimation algorithm, is possible to obtain an improvement in the accuracy of the MMS navigation solution as also in the global consistency of the final point cloud. This improvement is measured in about 7 cm for planimetric coordinate and 34 cm along the elevation with respect to the use of the Stencil system alone.

Trajectory estimation based on extended state observer with Fal-filter

2015 ◽  
Vol 119 (1218) ◽  
pp. 1017-1031 ◽  
Author(s):  
C-L Lin ◽  
S-L Hsieh ◽  
Y-P Lin
Keyword(s):  
Computer Simulation ◽  
Estimation Algorithm ◽  
State Observer ◽  
Second Order ◽  
Extended State Observer ◽  
Terminal Phase ◽  
Extended State ◽  
Target Information ◽  
Trajectory Estimation ◽  
Target Trajectory

Abstract This paper intends to develop a target trajectory estimation algorithm with application to the ballistic target estimation in the terminal phase. The proposed design is based on the application of a second-order extended state observer (ESO) technique using target information acquired from the seeker to estimate the trajectory of manoeuverable ballistic targets. Satisfactory results have been received while applying the design in estimation of either two-dimenional or three-dimentional target evasive acceleration via computer simulation.

scholarly journals Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement Units

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4194 ◽  
Author(s):  
Markus Zrenner ◽  
Stefan Gradl ◽  
Ulf Jensen ◽  
Martin Ullrich ◽  
Bjoern Eskofier
Keyword(s):  
Motion Tracking ◽  
Stride Length ◽  
Positive Impact ◽  
Estimation Algorithm ◽  
Velocity Estimation ◽  
Measurement Unit ◽  
Trajectory Estimation ◽  
Inertial Measurement ◽  
Using Data ◽  
Foot Trajectory

Running has a positive impact on human health and is an accessible sport for most people. There is high demand for tracking running performance and progress for amateurs and professionals alike. The parameters velocity and distance are thereby of main interest. In this work, we evaluate the accuracy of four algorithms, which calculate the stride velocity and stride length during running using data of an inertial measurement unit (IMU) placed in the midsole of a running shoe. The four algorithms are based on stride time, foot acceleration, foot trajectory estimation, and deep learning, respectively. They are compared using two studies: a laboratory-based study comprising 2377 strides from 27 subjects with 3D motion tracking as a reference and a field study comprising 12 subjects performing a 3.2-km run in a real-world setup. The results show that the foot trajectory estimation algorithm performs best, achieving a mean error of 0.032 ± 0.274 m/s for the velocity estimation and 0.022 ± 0.157 m for the stride length. An interesting alternative for systems with a low energy budget is the acceleration-based approach. Our results support the implementation decision for running velocity and distance tracking using IMUs embedded in the sole of a running shoe.

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