WFMHT Method of Orbit Maneuver Detection Based on Space-Based Bearing-Only Measurement

The method of orbit maneuver detection of space targets is investigated using the space-based bearing-only measurement, which aims to acquire a real-time or nearly real-time awareness of orbit maneuver in the space situation awareness. First, the model for estimating real-time motion of a space target is presented, which only uses the space-based bearing-only measurements. The innovation characteristics of the normal orbit and orbit maneuver are analyzed and compared. Second, based on the hypothesis test methods of the distribution characteristic of the stochastic sequence, the WFMHT (i.e., weighted fusion of multi hypothesis tests) method with the innovation is put forward to detect the orbit maneuver. Furthermore, the criterion of determining the weight coefficients is studied. Finally, the method is validated by numeric simulations. The results show that the highest gained success rate is up to 36% with the WFMHT method than the prevalent Chi2 method. With the WFMHT method, the detection system achieves a strengthened robustness with greatly shortened detection window. The research will be beneficial to construction of our space situation awareness system.

Download Full-text

Real-Time Motion Tracking Detection System for a Spherical Pendulum Using a USB Camera

Transactions of the Korean Society of Mechanical Engineers A ◽

10.3795/ksme-a.2016.40.9.807 ◽

2016 ◽

Vol 40 (9) ◽

pp. 807-813

Author(s):

Byung-Yoon Moon ◽

Sung-Rak Hong ◽

Manh-Tuan Ha ◽

Chul-Goo Kang

Keyword(s):

Real Time ◽

Motion Tracking ◽

Detection System ◽

Spherical Pendulum ◽

Time Motion ◽

Usb Camera

Download Full-text

Application of deep learning in automatic detection of technical and tactical indicators of table tennis

PLoS ONE ◽

10.1371/journal.pone.0245259 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0245259

Author(s):

Fufeng Qiao

Keyword(s):

Deep Learning ◽

Real Time ◽

Short Term Memory ◽

Detection System ◽

Minimum Mean Square Error ◽

Automatic Detection ◽

Table Tennis ◽

Time Dynamic ◽

Time Motion ◽

Automatic Detection System

A DCNN-LSTM (Deep Convolutional Neural Network-Long Short Term Memory) model is proposed to recognize and track table tennis’s real-time trajectory in complex environments, aiming to help the audiences understand competition details and provide a reference for training enthusiasts using computers. Real-time motion features are extracted via deep reinforcement networks. DCNN tracks the recognized objects, and the LSTM algorithm predicts the ball’s trajectory. The model is tested on a self-built video dataset and existing systems and compared with other algorithms to verify its effectiveness. Finally, an overall tactical detection system is built to measure ball rotation and predict ball trajectory. Results demonstrate that in feature extraction, the Deep Deterministic Policy Gradient (DDPG) algorithm has the best performance, with a maximum accuracy rate of 89% and a minimum mean square error of 0.2475. The accuracy of target tracking effect and trajectory prediction is as high as 90%. Compared with traditional methods, the performance of the DCNN-LSTM model based on deep learning is improved by 23.17%. The implemented automatic detection system of table tennis tactical indicators can deal with the problems of table tennis tracking and rotation measurement. It can provide a theoretical foundation and practical value for related research in real-time dynamic detection of balls.

Download Full-text