The trajectory prediction of spacecraft by grey method

Wang, Qiyue; Zhang, Zili; Wang, Zhongyu; Wang, Yanqing; Zhou, Weihu

doi:10.1088/0957-0233/27/8/085011

Cited by 23 publications

(12 citation statements)

References 27 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, the ECGM(1,1) model of exponential curve optimization, PCGM(1,1) model of polynomial curve optimization in [ 39 ], and original GM(1,1) model were compared with RMGM(1,1). Second, the metabolic MGM(1,1) and GDF [ 47 ] were compared with RMGM(1,1). Third, the local path adjustment scheme was implemented according to the prediction results of the RMGM(1,1) model.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Complex Environment Path Planning for Unmanned Aerial Vehicles

Zhang

Ji-wu

Yang

et al. 2021

Sensors

View full text Add to dashboard Cite

Flying safely in complex urban environments is a challenge for unmanned aerial vehicles because path planning in urban environments with many narrow passages and few dynamic flight obstacles is difficult. The path planning problem is decomposed into global path planning and local path adjustment in this paper. First, a branch-selected rapidly-exploring random tree (BS-RRT) algorithm is proposed to solve the global path planning problem in environments with narrow passages. A cyclic pruning algorithm is proposed to shorten the length of the planned path. Second, the GM(1,1) model is improved with optimized background value named RMGM(1,1) to predict the flight path of dynamic obstacles. Herein, the local path adjustment is made by analyzing the prediction results. BS-RRT demonstrated a faster convergence speed and higher stability in narrow passage environments when compared with RRT, RRT-Connect, P-RRT, 1-0 Bg-RRT, and RRT*. In addition, the path planned by BS-RRT through the use of the cyclic pruning algorithm was the shortest. The prediction error of RMGM(1,1) was compared with those of ECGM(1,1), PCGM(1,1), GM(1,1), MGM(1,1), and GDF. The trajectory predicted by RMGM(1,1) was closer to the actual trajectory. Finally, we use the two methods to realize path planning in urban environments.

show abstract

Section: Experiments and Discussionmentioning

confidence: 99%

Complex Environment Path Planning for Unmanned Aerial Vehicles

Zhang

Ji-wu

Yang

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In the present experiment, the movement of the SFT model was measured through the image processing method, which is a nonintrusive technology with high-precision trajectory prediction of a moving object [18,19]. The images analyzed were captured using a digital camera with a frequency of 60 Hz, such that the time interval between two frames was 0.0167 s. The spatial resolution of the image is 1920 × 1024 pixels and the measurement accuracy of the movement is ±0.1 mm.…”

Section: Motion Analysis Methodsmentioning

confidence: 99%

Experimental Study on 2D Motion Characteristics of Submerged Floating Tunnel in Waves

Yang

Zhang

et al. 2020

JMSE

View full text Add to dashboard Cite

Submerged floating tunnel (SFT) is a new type of transportation infrastructure for crossing sea straits in relatively deeper water. Compared with the fixed tunnel, the main challenge in designing a SFT is the stability maintaining in a complex hydrodynamic environment, especially for the wave-induced dynamic load. In this study, a series of systematic experiments were conducted to investigate the 2D motion characteristics (i.e., heave, sway and roll) of the SFT exposed to regular waves. The movement of the SFT model is measured by the image processing method which is a noncontact measurement. The experimental observation of SFT motion during the process of wave and SFT interaction is described in detail, and the influence of several governing parameters is thoroughly analyzed, including the wave height and period, submergence depth, buoyancy to weight ratio (BWR), and the mooring line angle. The results show that the motion amplitudes of SFT increase with the wave height increasing. The effect of wave period is related to the natural period of the structure. The sway, heave and roll of the SFT submerged beneath the water surface are much smaller than that of the SFT on the water surface. With the increase of BWR, the motion of SFT decreases. The motion amplitude increases with mooring line angle increasing. Finally, empirical equations are proposed to estimate the motion characteristics of the SFT.

show abstract

“…LSTMs are eligible to detect the interconnection among previous and current data, also appointing that connection in the mode of memorized or learned weights [11]. The trajectory prediction approach, termed as a grey dynamic filter, which merges dynamic measurement theory and the theory of grey system is suggested according to [12], an emulation of symmetric/asymmetric accelerated motion carried out.…”

Section: Related Work and Previous Studiesmentioning

confidence: 99%

Deep Learning-Based Approach for Detecting Trajectory Modifications of Cassini-Huygens Spacecraft

et al. 2021

View full text Add to dashboard Cite

There were necessary trajectory modifications of Cassini spacecraft during its last 14 years movement cycle of the interplanetary research project. In the scale of tens of minutes of signal propagation time and 1.4-billion-kilometer size of Earth-Cassini channel, Complex Event Detection in the orbit modifications requires special investigation and analysis of the collected big data. For energy saving considerations, the communication between the Earth and Cassini was executed in non-periodic mode. This paper provides a sophisticated in-depth learning approach for detecting Cassini spacecraft trajectory modifications in post-processing mode. The model utilizes the ability of Long Short Term Memory (LSTM) neural networks for drawing out useful data and learning the time series inner data pattern, along with the forcefulness of LSTM layers for distinguishing dependencies among the long-short term. Our research study exploited the statistical rates (MCC, F1) to evaluate our models. We carried out multiple tests and evaluated the provided approach against several advanced models. The preparatory analysis showed that exploiting the LSTM layer provides a notable boost in rising the detection process performance. The proposed model achieved a number of 232 trajectory modification detections with 99.98 % accuracy among the last 13.5 years of the Cassini spacecraft life. INDEX TERMS artificial intelligence, Cassini-Huygens interplanetary project, complex event, sensory data, big data, neural network, pattern processing, knowledge representation.

show abstract

The trajectory prediction of spacecraft by grey method

Cited by 23 publications

References 27 publications

Complex Environment Path Planning for Unmanned Aerial Vehicles

Complex Environment Path Planning for Unmanned Aerial Vehicles

Experimental Study on 2D Motion Characteristics of Submerged Floating Tunnel in Waves

Deep Learning-Based Approach for Detecting Trajectory Modifications of Cassini-Huygens Spacecraft

Contact Info

Product

Resources

About