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Article  |  2022-04-27

Advanced Antennas Push Forward Wireless Connectivity

Kwai Man Luk ,   Baoyan Duan  

Editorial  |  2022-04-22

Artificial intelligence in impact damage evaluation of space debris for spacecraft

Since the first artificial satellite was launched in 1957, increasing human space activities have led to a deteriorating space debris environment. A huge amount of tiny space debris (from millimeter to micron level) appears in the Earth’s orbit, and its hypervelocity impact will cause serious damage to the structure and functional units of the spacecraft, including cabin’s outer surface, thermal barrier materials, thermal control coatings, solar panels, pipes, and cables. To ensure the safe operation of spacecraft and the completion of space missions, it is necessary to detect and evaluate the impact damage caused by space debris to provide risk warning and timely repair. Due to the complex outer surface materials of spacecraft and the unpredictability of impact damage events, the collected damage detection data present various complex characteristic information. Traditional damage identification and evaluation methods based on manual extraction of feature parameters have difficulty in accurately describing the above complex feature information. In recent years, the application of artificial intelligence (AI) technology in space debris impact perception, damage detection, risk assessment, etc. has begun to receive extensive attention from scholars and engineers, and some breakthroughs have been made in solving such very difficult engineering and technical problems. However, there are still many difficult problems to be solved in the application of AI technology to deal with the issue of space debris. With this background, several important tendencies have emerged in the use of AI methods for spacecraft damage detection and evaluation. 1. Various AI learning algorithms (such as neural networks and deep learning) are used and combined to effectively detect and classify damage features. AI learns in a variety of ways, and each learning algorithm is good at solving different problems. Combining multiple AI learning algorithms in different scenarios can improve detection efficiency and classify damage features. 2. Modifications and enhancements to the learning algorithm are explored to perform damage pattern recognition and evaluation more accurately and effectively. To improve the performance of the learning algorithm, modifications and enhancements are essential. Modifications and enhancements to the algorithm itself, including the setting of the loss function, optimization of iterative steps, and judgment of termination conditions, will have a significant impact on the performance of the learning algorithm. In addition, the complex learning algorithm network itself has a large number of parameters that need to be optimized. In fact, the optimization method of network parameters has become one of the core factors that determine the performance of the learning algorithm. 3. AI learning algorithms and models should preferably be extended to suit spacecraft damage detection and evaluation systems. In combination with specific spacecraft damage detection and assessment systems, existing learning algorithms and models can be extended by, e.g., preprocessing the actual input test data to obtain better algorithm iterative calculation results, classifying different damage detection scenarios, applying different optimization modules to obtain better performance comparison test results, and giving reasonable classification criteria for damage assessment results. 4. AI technology is used to analyze the data characteristics of various spacecraft impact damage samples to guide the space debris protection design of spacecraft. The advantage of AI technology is that it can analyze typical characteristics from a large number of data samples. By analyzing the impact damage samples of various types of spacecraft and according to the detection data characteristics under different impact conditions, researchers can obtain the damage type and damage degree of the spacecraft’s space debris protection structure. Therefore, engineers can improve the safety of spacecraft in orbit by optimizing the protective structure of the spacecraft. 5. AI technology is used to model and analyze space debris to realize the monitoring, early warning, mitigation, and removal of space debris to reduce the impact of space debris on spacecraft. Using AI technology to model and analyze space debris has a stronger expressive ability, which can express complex and qualitative empirical knowledge that is difficult to describe with mathematical formulas. AI modeling can be modified and expanded according to the new understanding of space debris model knowledge, and the system can be more flexible to adapt to new needs. The clearer the modeling and analysis results of space debris are, the more accurate the monitoring, early warning, mitigation, and removal of debris impacts are, thereby greatly reducing the impact of space debris on spacecraft. In short, spacecraft damage feature extraction and damage assessment are critical to the development of the aerospace industry, and these challenges call for new methods and techniques to stimulate the continuous efforts of aerospace equipment research, pattern recognition, and AI. In this context, the journal has organized a special feature on the application of AI in the space environment and spacecraft. This special feature focuses on spacecraft damage detection and assessment methods based on AI learning from detection data, including the hierarchical correlation analysis of spacecraft damage characteristics and detection data, and the construction of spacecraft damage assessment models based on AI analysis methods. After a rigorous review process, five research articles were selected for this feature.

Weimin BAO ,   Chun YIN   et al.

Research Article  |  2022-04-22

Vibration-based hypervelocity impact identification and localization

Hypervelocity impact (HVI) vibration source identification and localization have found wide applications in many fields, such as manned spacecraft protection and machine tool collision damage detection and localization. In this paper, we study the synchrosqueezed transform (SST) algorithm and the texture color distribution (TCD) based HVI source identification and localization using impact images. The extracted SST and TCD image features are fused for HVI image representation. To achieve more accurate detection and localization, the optimal selective stitching features OSSST+TCD are obtained by correlating and evaluating the similarity between the sample label and each dimension of the features. Popular conventional classification and regression models are merged by voting and stacking to achieve the final detection and localization. To demonstrate the effectiveness of the proposed algorithm, the HVI data recorded from three kinds of high-speed bullet striking on an aluminum alloy plate is used for experimentation. The experimental results show that the proposed HVI identification and localization algorithm is more accurate than other algorithms. Finally, based on sensor distribution, an accurate four-circle centroid localization algorithm is developed for HVI source coordinate localization.

Jiao BAO ,   Lifu LIU   et al.

Research Article  |  2022-04-22

Variational Bayesian multi-sparse component extraction for damage reconstruction of space debris hypervelocity impact

To improve the survivability of orbiting spacecraft against space debris impacts, we propose an impact method. First, a multi-area damage mining model, which can describe damages in different spatial layers, is built based on an infrared thermal image sequence. Subsequently, to identify different impact damage types from infrared image data effectively, the inference is used to solve for the parameters in the model. Then, an image-processing framework is proposed to eliminate errors and compare locations of different damage types. It includes an image segmentation algorithm with an energy function and an image fusion method with . In the experiment, the proposed method is used to evaluate the complex damages caused by the impact of the secondary debris cloud on the rear wall of the typical Whipple shield configuration. Experimental results show that it can effectively identify and evaluate the complex damage caused by , including surface and internal defects.

Xuegang HUANG ,   Anhua SHI   et al.

Research Article  |  2022-04-22

Damage quantitative assessment of spacecraft in a large-size inspection

To ensure the safety and reliability of spacecraft during multiple space missions, it is necessary to conduct in-situ nondestructive detection of the spacecraft to judge the damage caused by the of micrometeoroids and orbital debris (MMOD). In this paper, we propose an innovative method based on damage reconstructed image mosaic technology. First, a Gaussian mixture model clustering algorithm is applied to extract images that highlight damage characteristics. Then, a mosaicking scheme based on the ORB feature extraction algorithm and an improved M-estimator SAmple Consensus (MSAC) algorithm with an adaptive threshold selection method is proposed which can create large-scale mosaicked images for damage detection. Eventually, to create the mosaicked images, the damage characteristic regions are segmented and extracted. The location of the damage area is determined and the degree of damage is judged by calculating the centroid position and the perimeter quantitative parameters. The efficiency and applicability of the proposed method are verified by the experimental results.

Kuo ZHANG ,   Jianliang HUO   et al.