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An improved method for rockfall detection and tracking based on video stream

An improved method for rockfall detection and tracking based...

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IET International radar Conference (IRC 2023)

作者： Longyue Wang Songge Wang Xin Xie Yunkai Deng Weiming Tian Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing People's Republic of China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing People's Republic of China

ISBN: (数字)9781837240982

Rockfall events occur frequently in mountainous areas. To address the problems of missed detection, false detection, and trajectory interruption when using the deep learning-based online multiple object tracking methods to detect rockfalls, this paper proposes a rockfall detection and tracking method based on video streams. In the detection stage, three-frame difference method is utilized to obtain the moving targets from the video streams, and they are combined with the detection results of the rock detector obtained by the offline-trained YOLOX model. In the tracking stage, data association is firstly performed based on the rockfall detection results. For the existing trajectories that are not matched at the current moment, re-matching is performed by combining the moving object detection results to achieve accurate tracking of rockfalls. Simulations and field experiments prove that the detection method proposed in this paper can effectively separate the rockfalls in the video, and the detected rockfalls have high precision. Besides, it significantly improves the accuracy of rockfall tracking, effectively suppressing phenomena such as trajectory interruption during tracking.

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Transductive Multi-Prototype Network for HRRP Recognition with Missing Aspects

Transductive Multi-Prototype Network for HRRP Recognition wi...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Mingchen Yuan Jiaqi Liu Xinyang Wang Yang Li Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China Zhengzhou Academy Of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The High-Resolution Range Profile (HRRP) plays a pivotal role in radar automatic target recognition. However, due to its aspect sensitivity, the absence of aspects within the training data can lead to a decline in recognition accuracy. To mitigate this issue, we introduce a novel Transductive Multi-Prototype Network (TMP-Net), leveraging the intrinsic similarity within test data to propagate labels along a manifold, thereby achieving classification. TMP-Net addresses the aspect sensitivity by utilizing multiple prototypes per class. Additionally, a loss function term is proposed to ensure the existence of the manifold. Experiments on the MSATR dataset demonstrate that TMP-Net outperforms existing methods by 10.22% in recognition accuracy even with only 10% of aspects represented in the training set. Furthermore, analysis reveals the advantages of employing multiple prototypes.

关键词： Manifolds Training Sensitivity Accuracy Target recognition Prototypes Training data radar Data processing

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Missing Modality Completion for Multi Frequency radar HRRP Recognition Using GAN

Missing Modality Completion for Multi Frequency Radar HRRP R...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Qiang Zhou Bingqian Yu Yuanhao Meng Xin Zhang Yang Li Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

High-resolution range profile (HRRP) is one of the commonly used methods in radar automatic target recognition (RATR). Recently, obtaining HRRP under different modalities, such as frequency and polarization, to improve the RATR performance has become an important development direction. However, in practical applications, sensor failures or interference may lead to modality loss during the model testing phase, resulting in decreased performance or even causing the model to fail to function properly. This paper primarily addresses the scenario of frequency modality loss and specifically proposes a modality completion and recognition network with generative adversarial network-embedded (MCRN-GAN) model. The model first generates and completes the missing modality features using the frequency feature transformation loop (FFTL) based on GAN. Subsequently, it fuses multi-modal features based on spatial and channel attention mechanisms to obtain a joint representation of dual-frequency features. Finally, a fully connected layer is used to complete the classification. Experiments on Ku and W frequency modality datasets demonstrates that the proposed method can effectively handle missing modality data after sufficient training without significant performance loss.

关键词： Training Frequency locked loops Target recognition Fuses radar Interference Feature extraction Generative adversarial networks Data processing Testing

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Orbit Determination Error Analysis and Compensation for Multi-static GEO SAR Imaging

Orbit Determination Error Analysis and Compensation for Mult...

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IET International radar Conference 2023, IRC 2023

作者： Liu, Yan Zhang, Wentao Chen, Zhiyang Dong, Xichao Huang, Xiaotao Chen, Leping School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100084 China Beijing Institute of Remote Sensing Equipment Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9781839539954

The high orbit altitude of Geosynchronous Synthetic Aperture radar (GEO SAR) introduces a significant challenge of long synthetic aperture times, which restricts the application of rapid imaging. A multi-static GEO SAR system consisting of multiple satellites flying along track can be employed to jointly image, and thus, to reduce the synthetic aperture time, but the orbit determination errors of different satellites will bring serious defocusing effect to the sub-image synthesis. In this paper, the effects of orbit determination errors on sub-image offset, defocusing and constant phase errors are analyzed in the multi-static GEO SAR system, and an orbit determination error correction method based on phase gradient autofocus (PGA) and interferometric phase estimation is proposed. Simulation results validate the effectiveness of the method. © The Institution of Engineering & technology 2023.

关键词： Error compensation

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Acceleration method for target HRRP generation based on DDPM

Acceleration method for target HRRP generation based on DDPM

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Xitai Shen Qiang Zhou Bingqian Yu Xin Zhang Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The target high resolution range profile (HRRP) is the sum of the vector projected on the radar line of sight of the target scatters, which can reflect the radial structure characteristics of the target and is one of the important means of radar target recognition. Existing HRRP-based target recognition methods adopt the idea of machine learning classification, which usually require sufficient training samples to obtain satisfactory recognition performance. To solve the dilemma of insufficient radar HRRP data caused by factors such as non-cooperative target and complexity of data acquisition process, this paper uses denoising diffusion probabilistic models (DDPM) to generate HRRP data. Furthermore, to solve the problem that the DDPM generation speed is too slow to meet the actual needs, this paper designs a fast DDPM HRRP (FDH) data generation method based on progressive knowledge distillation. Finally, the experimental results show that the generation speed is increased by about 25 times compared with the initial model under the premise of losing 7.6% similarity.

关键词： Training Target recognition Noise reduction radar radar scattering Data collection Feature extraction Sampling methods Diffusion models Vectors

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Multi-Terrain radar HRRP Target Detection Using Contrastive Learning

Multi-Terrain Radar HRRP Target Detection Using Contrastive ...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Xinyang Wang Mingchen Yuan Jiaqi Liu Yang Li Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

As radar resolution improves, high-resolution radars have significantly enhanced target detection performance through the generation of high-resolution range profiles (HRRPs). However, when the range resolution is much smaller than the target, the target’s energy is dispersed across multiple range cells, leading to poor detection performance with traditional energy-based detection algorithms. Furthermore, existing detection algorithms are highly sensitive to clutter characteristics and exhibit poor adaptability to varying scenarios. To address these issues, this paper proposes a Constant False Alarm Rate (CFAR) HRRP detection algorithm based on contrastive learning for multiple terrains. The algorithm improves the contrastive learning loss function to achieve well-clustered representations of clutter samples during training. Test samples are evaluated by calculating the Mahalanobis distance between them and the training samples, and compared against detection thresholds to accomplish target detection. Testing results show that the proposed algorithm achieves superior detection performance across multiple terrains. Furthermore, to reach a 90% detection probability, the proposed method requires a signal-to-clutter ratio (SCR) reduction of 1 to 4 dB compared to the competing methods.

关键词： Training Energy resolution radar detection Object detection Contrastive learning radar Detection algorithms Clutter Signal to noise ratio Testing

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Open Set radar HRRP Recognition Using Confidence through Neural Weight Proximity

Open Set Radar HRRP Recognition Using Confidence through Neu...

引用

Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Yichen Liu Ping-An Liu Liang Zhang Yanhua Wang Zengdi Bao Yinhui Xu Radar Technology Research Institute Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China Xi’an Modern Control Technology Research Institute Xi’an China Zhengzhou Research Institute Beijing Institute of Technology Zhengzhou China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

High-resolution range profiles (HRRP) have become increasingly important in radar automatic target recognition (RATR) due to their ability to capture detailed structural features of targets. In practical RATR scenarios, the operational environment is open and dynamic, often exposing models to targets belonging to previously unseen categories. This underscores the necessity for open-set recognition (OSR) methods that can effectively distinguish both known and unknown targets. Theoretically, there exists a distributional discrepancy between the feature representations of known and unknown categories in deep learning models. By designing a suitable confidence calculation method that leverages this discrepancy, it becomes possible to accurately identify whether a given sample belongs to a known or an unknown category. Inspired by the Neural Collapse theory, we observe that deep features representing known categories tend to cluster tightly around the weight vectors of the final layer, whereas features from unknown categories are typically more scattered and lie farther away. Building on this observation, we propose a confidence measure that is determined by the proximity of a feature to the weight vectors of the final classification layer. Samples with confidence values below a defined threshold are classified as unknown, which forms the fundamental mechanism of our open-set detector. Our approach is versatile, making it applicable to a variety of network architectures and adaptable to different feature distance metrics. Extensive experiments conducted on HRRP data confirm the efficacy of our proposed method for open-set recognition, demonstrating its ability to reliably distinguish between known and unknown targets under diverse conditions.

关键词： Measurement Deep learning Weight measurement Training Target recognition radar Network architecture Vectors radar signal processing Reliability

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A Long-Term Joint Multi-Image Computerized Ionospheric Tomography Method Based on GEO SAR System

A Long-Term Joint Multi-Image Computerized Ionospheric Tomog...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yi Sui Xichao Dong Yuanhao Li Zhiyang Chen Cheng Hu Beijing Key Laboratory of Embedded Real-Time Information Processing Technology (Beijing Institute of Technology) Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Computerized Ionospheric Tomography (CIT) serves as a crucial method for ionospheric monitoring, playing a significant role in space environment surveillance and earthquake prediction. Existing CIT techniques are mostly based on the Global Navigation Satellite System (GNSS) system, constrained by the distribution of receivers. CIT based on geosynchronous SAR (GEO SAR) presents a solution by leveraging Persistent Scatterer (PS) points within the scene. However, current CIT techniques using GEO SAR typically utilize PS points from a single SAR image. This paper introduces a novel approach – a GEO SAR-based long-term joint multi-image CIT method. This method enhances the exploitation of satellite data, offers a broader range of observation angles, and improves tomography accuracy. Finally, through a CIT experiment involving three GEO SAR images, the electron density distribution is derived with a time resolution of 10 minutes. The results align with the International Reference Ionosphere (IRI) data, validating the feasibility and advantages of the proposed method. It is noteworthy that the proposed method caters to the tomography observation mode of a single satellite and is adaptable to each satellite within a satellite formation.

关键词： Global navigation satellite system Satellites Accuracy Surveillance Receivers Tomography radar polarimetry

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Dual-Band HRRP Fusion Recognition via Wavelet Decomposition embedded Autoencoder

Dual-Band HRRP Fusion Recognition via Wavelet Decomposition ...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Weijia Wang Zhuchang Qi Lue Wang Wei Yang Liang Zhang Yanhua Wang Radar Technology Research Institute Beijing Institute of Technology Beijing China Xi'an Institute of Modern Control Technology Xi'an China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

High-resolution range profile (HRRP) is a crucial technique for radar target recognition. Due to the coupling of azimuthal scattering information and aspect sensitivity, fine target recognition using HRRP remains challenging. One way to mitigate HRRP aspect sensitivity is through enhancing the dimensionality of radar information acquisition. Therefore, this paper proposes a Dual-Band HRRP Fusion Recognition Model via Wavelet Decomposition embedded Autoencoder (DF-WD-AENet). The method we proposed mainly comprises two modules. Firstly, the autoencoder model based on wavelet decomposition (WD-AEM) achieves constraint on feature information, making it better suited for dual-band fusion. Next is the dual-band information fusion module (DFM), in which we achieved the interaction and fusion of feature information through convolutional block attention module (CBAM) and omni-dimensional dynamic convolution (ODConv), and used a fusion loss function to train the model. Experiment shows that the recognition rate of our method is 7.73% higher than that of single-band, proving the effectiveness of the proposed method.

关键词： Sensitivity Target recognition Convolution Fuses Autoencoders Dual band Scattering radar radar signal processing Signal resolution

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Sparse View HRRP Recognition Based on Dual-Task of Generation and Recognition Method

Sparse View HRRP Recognition Based on Dual-Task of Generatio...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Yuanhao Meng Lue Wang Qiang Zhou Xin Zhang Liang Zhang Yanhua Wang Radar Technology Research Institute Beijing Institute of Technology Beijing China Xi'an Modern Control Technology Research Institute Xi'an China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In recent years, high-resolution range profiles (HRRPs) have played a significant role in radar target recognition. The pose sensitivity of HRRPs results in unsatisfactory target recognition capabilities under sparse viewing angles. However, for non-cooperative targets, obtaining full-view HRRPs is often challenging, leading to suboptimal performance in HRRP-based target recognition. To address the issue of target recognition under sparse viewing angles, we propose an end-to-end model DGR for sparse view recognition that integrates combined generation and recognition tasks. The DGR model extracts deep features of HRRPs through an attention mechanism and utilizes these features for both target recognition and the generation of adjacent-view HRRPs, with both tasks mutually reinforcing each other for improved performance in recognition and data generation. Furthermore, by employing domain adaptation methods, the model learns the variability of target HRRPs in the source domain and applies this knowledge to the target domain, enhancing its generalization ability. Experimental results demonstrate that the DGR model achieves optimal recognition and generation capabilities simultaneously.

关键词： Adaptation models Sensitivity Target recognition radar Data collection Feature extraction Generative adversarial networks Data augmentation Data models Data mining

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