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.
To improve the survivability of orbiting spacecraft against space debris impacts, we propose an impact damage assessment 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 variational Bayesian inference is used to solve for the parameters in the model. Then, an image-processing framework is proposed to eliminate variational Bayesian errors and compare locations of different damage types. It includes an image segmentation algorithm with an energy function and an image fusion method with sparse representation. 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 hypervelocity impact, including surface and internal defects.
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 hypervelocity impact of micrometeoroids and orbital debris (MMOD). In this paper, we propose an innovative quantitative assessment 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.
It is always a challenging task to model the trajectory and make an efficient damage estimation of debris clouds produced by hypervelocity impact (HVI) on thin-plates due to the difficulty in obtaining high-quality fragment images from experiments. To improve the damage estimation accuracy of HVIs on a typical double-plate Whipple shield configuration, we investigate the distributive characteristic of debris clouds in successive shadowgraphs using image processing techniques and traditional numerical methods. The aim is to extract the target movement parameters of a debris cloud from the acquired shadowgraphs using image processing techniques and construct a trajectory model to estimate the damage with desirable performance. In HVI experiments, eight successive frames of fragment shadowgraphs are derived from a hypervelocity sequence laser shadowgraph imager, and four representative frames are selected to facilitate the subsequent feature analysis. Then, using image processing techniques, such as denoising and segmentation techniques, special fragment features are extracted from successive images. Based on the extracted information, image matching of debris is conducted and the trajectory of debris clouds is modeled according to the matched debris. A comparison of the results obtained using our method and traditional numerical methods shows that the method of obtaining hypervelocity impact experimental data through image processing will provide critical information for improving numerical simulations. Finally, an improved estimation of damage to the rear wall is presented based on the constructed model. The proposed model is validated by comparing the estimated damage to the actual damage to the rear wall.
To detect spacecraft damage caused by hypervelocity impact, we propose an advanced spacecraft defect extraction algorithm based on infrared imaging detection. The Gaussian mixture model (GMM) is used to classify the temperature change characteristics in the sampled data of the infrared video stream and reconstruct the image to obtain the infrared reconstructed image (IRRI) reflecting the defect characteristics. The designed segmentation objective function is used to ensure the effectiveness of image segmentation results for noise removal and detail preservation, while taking into account the complexity of IRRI (that is, the required trade-offs are different). A multi-objective optimization algorithm is introduced to achieve balance between detail preservation and noise removal, and a multi-objective evolutionary algorithm based on decomposition (MOEA/D) is used for optimization to ensure damage segmentation accuracy. Experimental results verify the effectiveness of the proposed algorithm.
With the advent of Industry 4.0, water treatment systems (WTSs) are recognized as typical industrial cyber-physical systems (iCPSs) that are connected to the open Internet. Advanced information technology (IT) benefits the WTS in the aspects of reliability, efficiency, and economy. However, the vulnerabilities exposed in the communication and control infrastructure on the cyber side make WTSs prone to cyber attacks. The traditional IT system oriented defense mechanisms cannot be directly applied in safety-critical WTSs because the availability and real-time requirements are of great importance. In this paper, we propose an entropy-based intrusion detection (EBID) method to thwart cyber attacks against widely used controllers (e.g., programmable logic controllers) in WTSs to address this issue. Because of the varied WTS operating conditions, there is a high false-positive rate with a static threshold for detection. Therefore, we propose a dynamic threshold adjustment mechanism to improve the performance of EBID. To validate the performance of the proposed approaches, we built a high-fidelity WTS testbed with more than 50 measurement points. We conducted experiments under two attack scenarios with a total of 36 attacks, showing that the proposed methods achieved a detection rate of 97.22% and a false alarm rate of 1.67%.
This paper proposes a combination weighting (CW) model based on iMOEA/D-DE (i.e., improved multiobjective evolutionary algorithm based on decomposition with differential evolution) with the aim to accurately compute the weight of evaluation methods. Multi-expert weight considers only subjective weights, leading to poor objectivity. To overcome this shortcoming, a multiobjective optimization model of CW based on improved game theory is proposed while considering the uncertainty of combination coefficients. An improved mutation operator is introduced to improve the convergence speed, and thus better optimization results are obtained. Meanwhile, an adaptive mutation constant and crossover probability constant with self-learning ability are proposed to improve the robustness of MOEA/D-DE. Since the existing weight evaluation approaches cannot evaluate weights separately, a new weight evaluation approach based on relative entropy is presented. Taking the evaluation method of integrated navigation systems as an example, certain experiments are carried out. It is proved that the proposed algorithm is effective and has excellent performance.
To deal with the threat of the new generation of electronic warfare, we establish a non-cooperative countermeasure game model to analyze power allocation and interference suppression between multistatic multipleinput multiple-output (MIMO) radars and multiple jammers in this study. First, according to the power allocation strategy, a supermodular power allocation game framework with a fixed weight (FW) vector is constructed. At the same time, a constrained optimization model for maximizing the radar utility function is established. Based on the utility function, the best power allocation strategies for the radars and jammers are obtained. The existence and uniqueness of the Nash equilibrium (NE) of the supermodular game are proved. A supermodular game algorithm with FW is proposed which converges to the NE. In addition, we use adaptive beamforming methods to suppress cross-channel interference that occurs as direct wave interferences between the radars and jammers. A supermodular game algorithm for joint power allocation and beamforming is also proposed. The algorithm can ensure the best power allocation, and also improves the interference suppression ability of the MIMO radar. Finally, the effectiveness and convergence of two algorithms are verified by numerical results.
As a classic deep learning target detection algorithm, Faster R-CNN (region convolutional neural network) has been widely used in high-resolution synthetic aperture radar (SAR) and inverse SAR (ISAR) image detection. However, for most common low-resolution radar plane position indicator (PPI) images, it is difficult to achieve good performance. In this paper, taking navigation radar PPI images as an example, a marine target detection method based on the Marine-Faster R-CNN algorithm is proposed in the case of complex background (e.g., sea clutter) and target characteristics. The method performs feature extraction and target recognition on PPI images generated by radar echoes with the convolutional neural network (CNN). First, to improve the accuracy of detecting marine targets and reduce the false alarm rate, Faster R-CNN was optimized as the Marine-Faster R-CNN in five respects: new backbone network, anchor size, dense target detection, data sample balance, and scale normalization. Then, JRC (Japan Radio Co., Ltd.) navigation radar was used to collect echo data under different conditions to build a marine target dataset. Finally, comparisons with the classic Faster R-CNN method and the constant false alarm rate (CFAR) algorithm proved that the proposed method is more accurate and robust, has stronger generalization ability, and can be applied to the detection of marine targets for navigation radar. Its performance was tested with datasets from different observation conditions (sea states, radar parameters, and different targets).
The three-dimensional localization problem for noncircular sources in near-field with a centro-symmetric cross array is rarely studied. In this paper, we propose an algorithm with improved estimation performance. We decompose the multiple parameters of the steering vector in a specific order so that it can be converted into the products of several matrices, and each of the matrices includes only one parameter. On this basis, each parameter to be resolved can be estimated by performing a one-dimensional spatial spectral search. Although the computational complexity of the proposed algorithm is several times that of our previous algorithm, the estimation performance, including its error and resolution, with respect to the direction of arrival, is improved, and the range estimation performance can be maintained. The superiority of the proposed algorithm is verified by simulation results.
In the practice of clinical endoscopy, the precise estimation of the lesion size is quite significant for diagnosis. In this paper, we propose a three-dimensional (3D) measurement method for binocular endoscopes based on deep learning, which can overcome the poor robustness of the traditional binocular matching algorithm in texture-less areas. A simulated binocular image dataset is created from the target 3D data obtained by a 3D scanner and the binocular camera is simulated by 3D rendering software to train a disparity estimation model for 3D measurement. The experimental results demonstrate that, compared with the traditional binocular matching algorithm, the proposed method improves the accuracy and disparity map generation speed by 48.9% and 90.5%, respectively. This can provide more accurate and reliable lesion size and improve the efficiency of endoscopic diagnosis.