Neural-Network-Based Autonomous Star Identification Algorithm

2000 ◽  
Vol 23 (4) ◽  
pp. 728-735 ◽  
Author(s):  
Jian Hong ◽  
Julie A. Dickerson
Keyword(s):  
Neural Network ◽  
Identification Algorithm ◽  
Star Identification

scholarly journals Efficient Star Identification Using a Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3684
Author(s):  
David Rijlaarsdam ◽  
Hamza Yous ◽  
Jonathan Byrne ◽  
Davide Oddenino ◽  
Gianluca Furano ◽  
...  
Keyword(s):  
Neural Network ◽  
Attitude Determination ◽  
Lightweight Design ◽  
A Priori ◽  
Search Time ◽  
Identification Algorithm ◽  
Star Sensor ◽  
Feature Extraction Method ◽  
Star Identification ◽  
Space Star

The required precision for attitude determination in spacecraft is increasing, providing a need for more accurate attitude determination sensors. The star sensor or star tracker provides unmatched arc-second precision and with the rise of micro satellites these sensors are becoming smaller, faster and more efficient. The most critical component in the star sensor system is the lost-in-space star identification algorithm which identifies stars in a scene without a priori attitude information. In this paper, we present an efficient lost-in-space star identification algorithm using a neural network and a robust and novel feature extraction method. Since a neural network implicitly stores the patterns associated with a guide star, a database lookup is eliminated from the matching process. The search time is therefore not influenced by the number of patterns stored in the network, making it constant (O(1)). This search time is unrivalled by other star identification algorithms. The presented algorithm provides excellent performance in a simple and lightweight design, making neural networks the preferred choice for star identification algorithms.

scholarly journals An Efficient and Robust Star Identification Algorithm Based on Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7686
Author(s):  
Bendong Wang ◽  
Hao Wang ◽  
Zhonghe Jin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Identification Accuracy ◽  
Identification Algorithm ◽  
Rotation Invariant ◽  
One Dimensional ◽  
Identification Rate ◽  
Star Identification ◽  
Space Star ◽  
Fully Connected

A lost-in-space star identification algorithm based on a one-dimensional Convolutional Neural Network (1D CNN) is proposed. The lost-in-space star identification aims to identify stars observed with corresponding catalog stars when there is no prior attitude information. With the help of neural networks, the robustness and the speed of the star identification are improved greatly. In this paper, a modified log-Polar mapping is used to constructed rotation-invariant star patterns. Then a 1D CNN is utilized to classify the star patterns associated with guide stars. In the 1D CNN model, a global average pooling layer is used to replace fully-connected layers to reduce the number of parameters and the risk of overfitting. Experiments show that the proposed algorithm is highly robust to position noise, magnitude noise, and false stars. The identification accuracy is 98.1% with 5 pixels position noise, 97.4% with 5 false stars, and 97.7% with 0.5 Mv magnitude noise, respectively, which is significantly higher than the identification rate of the pyramid, optimized grid and modified log-polar algorithms. Moreover, the proposed algorithm guarantees a reliable star identification under dynamic conditions. The identification accuracy is 82.1% with angular velocity of 10 degrees per second. Furthermore, its identification time is as short as 32.7 miliseconds and the memory required is about 1920 kilobytes. The algorithm proposed is suitable for current embedded systems.

STAR IDENTIFICATION ALGORITHM FOR UNCALIBRATED, WIDE FOV CAMERAS

2015 ◽  
Vol 149 (6) ◽  
pp. 182 ◽  
Author(s):  
Mohamad Javad Ajdadi ◽  
Mahdi Ghafarzadeh ◽  
Mojtaba Taheri ◽  
Ehsan Mosadeq ◽  
Mahdi Khakian Ghomi
Keyword(s):  
Identification Algorithm ◽  
Star Identification ◽  
Wide Fov

A Non - Intrusive Load Identification Algorithm Combined with Event Detection

2021 ◽  
Vol 14 ◽  
Author(s):  
Runhai Jiao ◽  
Qihang Zhou ◽  
Liangqiu Lyu ◽  
Guangwei Yan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
The State ◽  
Event Recognition ◽  
Identification Algorithm ◽  
Load Identification ◽  
Fusion Model ◽  
Public Data ◽  
Artificial Neural

Background: The traditional state-based non-intrusive load monitoring method mainly deploys the aggregate load as the characteristic to identify the states of every electrical appliance. Each identification is relatively independent, and there is no correlation between the identification results. Objective: This paper combines the event detection results with the state-based non-intrusive load identification algorithm to improve accuracy. Methods: Firstly, the load recognition model based on an artificial neural network is constructed, and the state-based recognition results are obtained. An event recognition and detection model is then built to identify electrical state transitions, that is, the current moment based on the event recognition results obtained from the previous moment. Finally, a reasonable decision method is constructed to determine the identification result of the electrical states. Result: Experimental results on the public data set REDD show that in the Long Short-Term Memory (LSTM) fusion model, the macro-F1 is increased by an average of 6%, and the macro-F1 of the Artificial Neural Network (ANN) fusion model is increased by an average of 5.3% compared with LSTM and ANN. Conclusion: The proposed model can effectively improve the accuracy of identification compared with the state-based load identification method.

scholarly journals Nonlinear Aeroelastic System Identification Based on Neural Network

2018 ◽  
Vol 8 (10) ◽  
pp. 1916
Author(s):  
Bo Zhang ◽  
Jinglong Han ◽  
Haiwei Yun ◽  
Xiaomao Chen
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
System Identification ◽  
Control Surface ◽  
Identification Algorithm ◽  
Aeroelastic System ◽  
Structural Nonlinearity ◽  
Time Marching ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network

This paper focuses on the nonlinear aeroelastic system identification method based on an artificial neural network (ANN) that uses time-delay and feedback elements. A typical two-dimensional wing section with control surface is modelled to illustrate the proposed identification algorithm. The response of the system, which applies a sine-chirp input signal on the control surface, is computed by time-marching-integration. A time-delay recurrent neural network (TDRNN) is employed and trained to predict the pitch angle of the system. The chirp and sine excitation signals are used to verify the identified system. Estimation results of the trained neural network are compared with numerical simulation values. Two types of structural nonlinearity are studied, cubic-spring and friction. The results indicate that the TDRNN can approach the nonlinear aeroelastic system exactly.

scholarly journals An End-to-End Identification Algorithm for Smearing Star Image

2021 ◽  
Vol 13 (22) ◽  
pp. 4541
Author(s):  
Jinliang Han ◽  
Xiubin Yang ◽  
Tingting Xu ◽  
Zongqiang Fu ◽  
Lin Chang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Semantic Information ◽  
The Self ◽  
Identification Algorithm ◽  
Identification Rate ◽  
Star Image ◽  
Star Identification ◽  
Angular Velocities ◽  
End To End

In the previous study, there were a few direct star identification (star-ID) algorithms for smearing star image. An end-to-end star-ID algorithm is proposed in this article, to directly identify the smearing image from star sensors with fast attitude maneuvering. Combined with convolutional neural networks and the self-attention mechanism of transformer encoder, the algorithm can effectively classify the smearing image and identify the star. Through feature extraction and position encoding, neural networks learn the position of stars to generate semantic information and realize the end-to-end identification for the smearing star image. The algorithm can also solve the problem of low identification rate due to smearing of long exposure time for images. A dataset of dynamic stars is analyzed and constructed based on multiple angular velocities. Experiment results show that, compared with representative algorithms, the identification rate of the proposed algorithm is improved at high angular velocities. When the three-axis angular velocity is 10°/s, the rate is still 60.4%. At the same time, the proposed algorithm has good robustness to position noise and magnitude noise.

Fast star identification algorithm based on multi-feature matching

2019 ◽  
Vol 27 (8) ◽  
pp. 1870-1879
Author(s):  
王 军 WANG Jun ◽  
何 昕 HE Xin ◽  
魏仲慧 WEI Zhong-hui ◽  
吕 游 L You ◽  
穆治亚 MU Zhi-ya
Keyword(s):  
Feature Matching ◽  
Identification Algorithm ◽  
Star Identification

An identification algorithm of driver steering characteristics based on backpropagation neural network

2019 ◽  
Vol 233 (9) ◽  
pp. 2333-2342
Author(s):  
Shangyu Zhao ◽  
Guoying Chen ◽  
Min Hua ◽  
Changfu Zong
Keyword(s):  
Neural Network ◽  
Driving Simulator ◽  
Careful Analysis ◽  
Angular Speed ◽  
Identification Accuracy ◽  
Steering Wheel ◽  
Identification Algorithm ◽  
Backpropagation Neural Network ◽  
Feature Parameter ◽  
Steering Characteristics

This paper presents a novel identification method of driver steering characteristics based on backpropagation neural network. First, a driving simulator is built to collect required driving data. After careful analysis, three feature parameters that reflect driver steering characteristics are determined, including the average steering wheel angular speed, the standard deviation of the steering wheel angle, and the average vehicle longitudinal speed. Then, steering feature parameter vectors are extracted from raw data and clustered by the K-means algorithm. According to the clustering result, driver steering characteristics are divided into three types: cautious, average, and aggressive. Subsequently, a backpropagation neural network with two hidden layers is designed and trained to identify the types of feature parameter vectors. Verification results show that the established backpropagation neural network has high identification accuracy and good generalization ability for the identification of driver steering characteristics.

scholarly journals Research on Adaptive Neural Network Control System Based on Nonlinear U-Model with Time-Varying Delay

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Fengxia Xu ◽  
Yao Cheng ◽  
Hongliang Ren ◽  
Shili Wang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Delay System ◽  
Network Control ◽  
Identification Algorithm ◽  
Time Varying ◽  
Adaptive Neural Network ◽  
Time Varying Delay ◽  
Adaptive Neural Network Control ◽  
Varying Delay

U-model can approximate a large class of smooth nonlinear time-varying delay system to any accuracy by using time-varying delay parameters polynomial. This paper proposes a new approach, namely, U-model approach, to solving the problems of analysis and synthesis for nonlinear systems. Based on the idea of discrete-time U-model with time-varying delay, the identification algorithm of adaptive neural network is given for the nonlinear model. Then, the controller is designed by using the Newton-Raphson formula and the stability analysis is given for the closed-loop nonlinear systems. Finally, illustrative examples are given to show the validity and applicability of the obtained results.

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