scholarly journals Low-Cost Sensors State Estimation Algorithm for a Small Hand-Launched Solar-Powered UAV

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4627 ◽  
Author(s):  
Guo ◽  
Zhou ◽  
Zhu ◽  
Bai
Keyword(s):  
State Estimation ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Position Estimation ◽  
Parameters Estimation ◽  
Estimation Accuracy ◽  
Estimation Algorithms ◽  
Full State ◽  
Small Hand ◽  
Solar Powered

In order to reduce the cost of the flight controller and improve the control accuracy of solar-powered unmanned aerial vehicle (UAV), three state estimation algorithms based on the extended Kalman filter (EKF) with different structures are proposed: Three-stage series, full-state direct and indirect state estimation algorithms. A small hand-launched solar-powered UAV without ailerons is used as the object with which to compare the algorithm structure, estimation accuracy, and platform requirements and application. The three-stage estimation algorithm has a position accuracy of 6 m and is suitable for low-cost small, low control precision UAVs. The precision of full-state direct algorithm is 3.4 m, which is suitable for platforms with low-cost and high-trajectory tracking accuracy. The precision of the full-state indirect method is similar to the direct, but it is more stable for state switching, overall parameters estimation, and can be applied to large platforms. A full-scaled electric hand-launched UAV loaded with the three-stage series algorithm was used for the field test. Results verified the feasibility of the estimation algorithm and it obtained a position estimation accuracy of 23 m.

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 Decentralized State Estimation Algorithm of Centralized Equivalent Precision for Formation Flying Spacecrafts Based on Junction Tree

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Mengyuan Dai ◽  
Hua Mu ◽  
Meiping Wu ◽  
Zhiwen Xian
Keyword(s):  
State Estimation ◽  
Formation Flying ◽  
Dynamic Bayesian Network ◽  
Estimation Algorithm ◽  
Junction Tree ◽  
Estimation Algorithms ◽  
Distributed Structure ◽  
Decentralized Algorithms ◽  
Formation Flying Spacecraft ◽  
Decentralized State Estimation

As centralized state estimation algorithms for formation flying spacecraft would suffer from high computational burdens when the scale of the formation increases, it is necessary to develop decentralized algorithms. To the state of the art, most decentralized algorithms for formation flying are derived from centralized EKF by simplification and decoupling, rendering suboptimal estimations. In this paper, typical decentralized state estimation algorithms are reviewed, and a new scheme for decentralized algorithms is proposed. In the new solution, the system is modeled as a dynamic Bayesian network (DBN). A probabilistic graphical method named junction tree (JT) is used to analyze the hidden distributed structure of the DBNs. Inference on JT is a decentralized form of centralized Bayesian estimation (BE), which is a modularized three-step procedure of receiving messages, collecting evidences, and generating messages. As KF is a special case of BE, the new solution based on JT is equivalent in precision to centralized KF in theory. A cooperative navigation example of a three-satellite formation is used to test the decentralized algorithms. Simulation results indicate that JT has the best precision among all current decentralized algorithms.

State and Parametric Estimation of Nonlinear Systems Described by Wiener Sate-Space Mathematical Models

2015 ◽  
pp. 107-145 ◽  
Author(s):  
Houda Salhi ◽  
Samira Kamoun
Keyword(s):  
Nonlinear Systems ◽  
Mathematical Models ◽  
State Estimation ◽  
Estimation Algorithm ◽  
The State ◽  
Parametric Estimation ◽  
State Variables ◽  
Estimation Algorithms ◽  
And Performance ◽  
The Stability

This chapter deals with the description, the parametric estimation, the state estimation, and the parametric and state estimation conjointly of nonlinear systems. The focus is on the class of nonlinear systems, which are described by Wiener state-space discrete-time mathematical models. Thus, the authors develop a new recursive parametric estimation algorithm, which is based on least squares techniques. The stability conditions of the developed parametric estimation scheme are analyzed using the Lyapunov method. The state estimation problem of the considered nonlinear systems is formulated. Thus, the authors propose a recursive state estimation algorithm, which is based on Kalman Filter. A new recursive algorithm is proposed, which permits one to estimate conjointly the parameters and the state variables of nonlinear systems described by Wiener mathematical models, with unknown parameters and state variables. The efficiency and performance of the proposed recursive estimation algorithms are tested on numerical simulation examples.

scholarly journals Model-Based 3D Pose Estimation of a Single RGB Image Using a Deep Viewpoint Classification Neural Network

2019 ◽  
Vol 9 (12) ◽  
pp. 2478 ◽  
Author(s):  
Jui-Yuan Su ◽  
Shyi-Chyi Cheng ◽  
Chin-Chun Chang ◽  
Jing-Ming Chen
Keyword(s):  
Neural Network ◽  
Pose Estimation ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Training Dataset ◽  
3D Pose Estimation ◽  
Estimation Algorithms ◽  
Model Based ◽  
3D Scene ◽  
Rgb Image

This paper presents a model-based approach for 3D pose estimation of a single RGB image to keep the 3D scene model up-to-date using a low-cost camera. A prelearned image model of the target scene is first reconstructed using a training RGB-D video. Next, the model is analyzed using the proposed multiple principal analysis to label the viewpoint class of each training RGB image and construct a training dataset for training a deep learning viewpoint classification neural network (DVCNN). For all training images in a viewpoint class, the DVCNN estimates their membership probabilities and defines the template of the class as the one of the highest probability. To achieve the goal of scene reconstruction in a 3D space using a camera, using the information of templates, a pose estimation algorithm follows to estimate the pose parameters and depth map of a single RGB image captured by navigating the camera to a specific viewpoint. Obviously, the pose estimation algorithm is the key to success for updating the status of the 3D scene. To compare with conventional pose estimation algorithms which use sparse features for pose estimation, our approach enhances the quality of reconstructing the 3D scene point cloud using the template-to-frame registration. Finally, we verify the ability of the established reconstruction system on publicly available benchmark datasets and compare it with the state-of-the-art pose estimation algorithms. The results indicate that our approach outperforms the compared methods in terms of the accuracy of pose estimation.

Load Position Estimation for Crane Anti-Sway Control Systems

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
Ulf Schaper ◽  
Oliver Sawodny ◽  
Michael Zeitz ◽  
Klaus Schneider
Keyword(s):  
Signal Processing ◽  
Control Systems ◽  
Estimation Algorithm ◽  
Position Estimation ◽  
Processing Algorithm ◽  
Estimation Accuracy ◽  
Suspension Point ◽  
Signal Processing Algorithm ◽  
Acceleration Signal ◽  
Multiple Test

A rising number of modern cranes are equipped with anti-sway control systems to facilitate crane operation, improve positioning accuracy, and increase turnover. Commonly, these industrial crane control systems require pendulum state information for feedback control. Therefore, a pendulum sway sensor (e.g., a rope-mounted gyroscope) and a signal processing algorithm are required. Such a signal processing algorithm needs to filter out disturbances from both the sensor and the crane, e.g., signal noise and string oscillations of a long rope. Typically, these signal processing algorithms require the knowledge of the acceleration of the rope suspension point. This acceleration signal is often estimated from drive models. When drive models are uncertain, the pendulum state estimation accuracy suffers from drive model inaccuracy. In this contribution, an improved estimation algorithm is presented which estimates the load position without relying on the rope suspension point acceleration. The developed Extended Kalman Filter is implemented on a Liebherr mobile harbor crane and its effectiveness is validated with multiple test rides and GPS load position reference measurements.

scholarly journals Compressive Sensing for High-Resolution Direction-of-Arrival Estimation via Iterative Optimization on Sensing Matrix

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Hongtao Li ◽  
Chaoyu Wang ◽  
Xiaohua Zhu
Keyword(s):  
Compressive Sensing ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Sensing Matrix ◽  
Dantzig Selector ◽  
Iterative Optimization ◽  
Estimation Algorithms ◽  
Sensing Model

A novel compressive sensing- (CS-) based direction-of-arrival (DOA) estimation algorithm is proposed to solve the performance degradation of the CS-based DOA estimation in the presence of sensing matrix mismatching. Firstly, a DOA sparse sensing model is set up in the presence of sensing matrix mismatching. Secondly, combining the Dantzig selector (DS) algorithm and least-absolute shrinkage and selection operator (LASSO) algorithm, a CS-based DOA estimation algorithm which performs iterative optimization alternatively on target angle information vector and sensing matrix mismatching error vector is proposed. The simulation result indicates that the proposed algorithm possesses higher angle resolution and estimation accuracy compared with conventional CS-based DOA estimation algorithms.

scholarly journals Tensor-Based DOA Estimation for Array Virtual Translation

2021 ◽  
Author(s):  
Jiaqiang Peng ◽  
Guimei Zheng
Keyword(s):  
Tensor Decomposition ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Processing Technique ◽  
Estimation Accuracy ◽  
Signal Subspace ◽  
Spatial Smoothing ◽  
Factor Matrix ◽  
Estimation Algorithms ◽  
Array Aperture

Abstract In order to make up for the problem that the tensor-based spatial smoothing DOA estimation algorithm cannot make good use of the physical aperture of the array, this paper proposes a tensor-based array virtual translation DOA estimation algorithm. Under the framework of the tensor-based DOA estimation algorithm, the algorithm applies the array virtual translation technique to the factor matrix obtained after tensor decomposition, which can be expanded into signal subspace and approximately has a Vandermonde structure. Furthermore, the available array aperture of the algorithm is expanded, the estimation accuracy is improved, and the limitation of the physical array aperture on the algorithm’s multi-target estimation ability is broken. Since the processing technique proposed in this paper has nothing to do with the construction of tensors, this technique is suitable for all DOA estimation algorithms based on tensors. Theoretical analysis and numerical simulation verify the effectiveness of the algorithm proposed in this paper.

scholarly journals Adaptive Estimation Algorithm for Correcting Low-Cost MEMS-SINS Errors of Unmanned Vehicles under the Conditions of Abnormal Measurements

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 623
Author(s):  
Lifei Zhang ◽  
Proletarsky Andrey Viktorovich ◽  
Maria Sergeevna Selezneva ◽  
Konstantin Avenirovich Neusypin
Keyword(s):  
Adaptive Estimation ◽  
Low Cost ◽  
Experimental Tests ◽  
Estimation Algorithm ◽  
Satellite System ◽  
Unmanned Vehicles ◽  
Estimation Accuracy ◽  
Accuracy Class ◽  
Unmanned Vehicle ◽  
The Impact

In this paper, a low-cost small-sized strap-down inertial navigation system (SINS)—Gyrolab GL-VG 109—is studied. When the system is installed on an unmanned vehicle and works in autonomous mode, it is difficult to determine the navigation parameters of the unmanned vehicle. Correcting the SINS information from the Global Navigation Satellite System (GNSS) can significantly increase the determination accuracy of the navigation parameters. However, this is only available when the GNSS signals are stable. A new adaptive estimation algorithm that can automatically detect, evaluate, and process the abnormal measurements is proposed in the present work. The determination of the navigation parameters can reach the third accuracy class using the proposed method. The effectiveness of the algorithm is verified by the mathematical simulation and the experimental tests (with a real SINS GL-VG 109), which are conducted in urban environments with a GNSS signal containing 15% and 40% abnormal measurements. The results show that the proposed method can significantly reduce the impact of abnormal measurements and improve the estimation accuracy.

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