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检索条件"机构=Beijing Key Laboratory of Embedded Real-time Information Processing Technology Radar Research Lab"

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An Adaptive Estimation Method for Group Target Number Based on information Theoretic Criterion

An Adaptive Estimation Method for Group Target Number Based ...

引用

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

作者： Libin Dou Qi Jiang Rui Wang Mengxin Shi Jichuan Zhang Radar Technology Research Institute School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Real-Time Information Processing Technology of Embedded Beijing China The Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The number of individuals within a group is crucial information for the correct association, robust tracking, and situation awareness. Traditional methods using the range profile’s peaks are not suitable when targets are densely distributed. The target number estimation algorithms based on monopulse systems have high computational complexity, while those based on array systems require additional sensor resources. To address these issues, this paper proposes a method based on multiple incoherent observations using one monopulse radar. By combining multiple incoherent observations from a single sensor with information theoretic criterion, this approach can estimate the number of targets even when the resolution of a single sensor is insufficient. The effectiveness of the algorithm is verified through simulations, and the algorithm's performance is analyzed.

关键词： Analytical models Target tracking Estimation radar radar tracking Data processing Adaptive estimation Computational complexity Signal resolution Sensor arrays

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labeled Random Finite Sets-Based Group Target Tracking with Multi-Detection Model

Labeled Random Finite Sets-Based Group Target Tracking with ...

引用

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

作者： Longxiang Jiao Qi Jiang Rui Wang Radar Technology Research Institute School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Real-Time Information Processing Technology of Embedded Beijing China The Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In radar tracking applications for low-altitude group targets (such as bird flocks or drone swarms), the traditional point target measurement model is often unsuitable due to the complex micro-Doppler signatures and multipath effects. Algorithms based on labeled random finite sets (RFS) challenges with multi-detection problems. This paper presents a new method for tracking low-altitude group targets using labeled RFS in radar systems. To mitigate the disturbance from multi-detection, we propose modified assignment cost matrices in the update process, which reduces the number of false associations, and thus achieves quality enhancement of trajectory estimations. The effectiveness of the proposed method is validated through simulation results.

关键词： Radio frequency Target tracking Costs Simulation Estimation radar radar tracking Trajectory Reliability Standards

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HRRP SCATTERING CENTER ESTIMATION BASED ON MODELLED NEURAL NETWORK

HRRP SCATTERING CENTER ESTIMATION BASED ON MODELLED NEURAL N...

引用

IET International radar Conference 2023, IRC 2023

作者： Zhang, Yu-Ang Zhou, Na Wang, Yanhua Zhang, Liang School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Electromagnetic Sensing Research Center of CEMEE State Key Laboratory Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Advanced Technology Research Institute Beijing Institute of Technology Shandong250300 China

ISBN: (纸本)9781839539954

Scattering center estimation of HRRP is crucial for radar automatic target recognition (RATR). Traditional estimation algorithms either necessitate prior information of the signal or suffer from mismatch issues or possess excessive computational costs. In this paper, we propose an HRRP-modelled neural network (HNN), which addresses the mismatch problem and exhibits better accuracy. HNN combines the HRRP signal model with its layered structure, including input, output and activation function. It is initialized via orthogonal matching pursuit and optimized using back propagation algorithm with dual learning rate. The estimated position and amplitude can be obtained by the weights between layers. Through simulations and experiments, we demonstrate the superiority of HNN over traditional methods. © The Institution of Engineering & technology 2023.

关键词： Backpropagation

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radar TARGET HRRP SEGMENTATION USING DIFFUSION MODEL

RADAR TARGET HRRP SEGMENTATION USING DIFFUSION MODEL

引用

IET International radar Conference 2023, IRC 2023

作者： Liu, Jiaqi Yuan, Mingchen Zhang, Liang Wang, Yanhua School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Electromagnetic Sensing Research Center of CEMEE State Key Laboratory Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Advanced Technology Research Institute Beijing Institute of Technology Shandong250300 China

ISBN: (纸本)9781839539954

High-resolution range profile (HRRP) formed by the extended target under high-resolution radar can reflect the detailed information of the target, which is one of the important means of target detection for air-to-ground radar. Accurately segmenting the target HRRP from the entire echo is of great significance for studying the target characteristics, target feature extraction and recognition. In this paper, a radar target HRRP segmentation algorithm using diffusion model is proposed. The disturbed position parameters of the target HRRP are generated according to the idea of adding noise by the diffusion model, and then the predicted segmentation results are obtained through the trained model to modify the disturbed parameters. The performance of the algorithm under different signal-to-noise ratios and polarization modes is evaluated in a measured dataset. The experimental results show that this method can effectively obtain target HRRPs and can be applied to the extended target segmentation of high-resolution radar. © The Institution of Engineering & technology 2023.

关键词： Signal to noise ratio

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DUAL BAND HRRP SCATTERING CENTER ESTIMATION VIA ATOMIC NORM MINIMIZATION

DUAL BAND HRRP SCATTERING CENTER ESTIMATION VIA ATOMIC NORM ...

引用

IET International radar Conference 2023, IRC 2023

作者： Zhou, Na Zhang, Yu-Ang Wang, Yanhua Zhang, Liang School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Electromagnetic Sensing Research Center of CEMEE State Key Laboratory Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Advanced Technology Research Institute Beijing Institute of Technology Shandong250300 China

ISBN: (纸本)9781839539954

High resolution range profile (HRRP) obtained through air-to-ground radar provides information on the target structure, which is widely used in military and aviation fields. On the other side, dual band radar is able to improve the performance of target detection and recognition by fusing the information of different band. In this paper, we propose a dual band HRRP scattering center estimation method based on atomic norm minimization (ANM). Due to the fact that dual band information reflects the characteristics of the same target, it is modelled as the multiple measurement vector (MMV) and incorporated into the modelling process to effectively utilize the correlation of dual band information and sparse reconstruction. This method estimates in a continuous domain, avoiding the problem of base mismatch. According to the experimental results, this method exhibits outstanding advantages in accuracy and robustness. © The Institution of Engineering & technology 2023.

关键词： radar target recognition

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Marine Ship Detection Method for SAR Image Based on Improved Faster RCNN

Marine Ship Detection Method for SAR Image Based on Improved...

引用

2021 SAR in Big Data Era, BIGSARDATA 2021

作者： Chai, Bingqian Chen, Liang Shi, Hao He, Cheng Beijing Key Laboratory of Embedded Real-time Information Processing Technology Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665401234

Due to the multi-view imaging principle of synthetic aperture radar (SAR), its imaging process is not limited by any time or any bad weather. The detection of marine ship for SAR image is a very important application in both military and private applications. A marine ship detection method in SAR image about an improved Faster RCNN-based approach is proposed. First, the deepest semantic features of SAR image extracted by Faster RCNN contain less target information and small targets may be ignored, so that we fuse the deepest feature and shallower features in the feature extraction network;Secondly, because the redundant information in the features will produce false alarms, we embed the Convolutional Block Attention Module (CBAM) in the feature extraction network to extract more effective features;Finally, we use bilinear interpolation to obtain floating-point coordinates to optimize RoI pooling which uses rounding quantization, so the error mapping caused by quantization will be reduced. The algorithm of this paper is tested on the SSDD public dataset, and the AP is increased from 0.79 to 0.89. The results demonstrate that the algorithm introduced in this paper has a very significant performance improvement for detecting ship-like targets from SAR images. © 2021 IEEE.

关键词： Synthetic aperture radar

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An improved ISAR imaging method under low SNR 14

An improved ISAR imaging method under low SNR

引用

14th European Conference on Synthetic Aperture radar, EUSAR 2022

作者： Gao, Yongpeng Ding, Zegnag Zhang, Tianyi Liu, Shujiang Guo, Xin Li, Pucheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401147 China DFH Satellite Co. Ltd Beijing100094 China

ISBN: (纸本)9783800758234

Current inverse synthetic aperture radar (ISAR) imaging methods cannot perform well when the signal-To-noise ratio (SNR) is low. In this paper, an improved ISAR imaging method based on generalized Radon-Fourier transform (GRFT) and relaxation (RELAX) technique is proposed. Through the proposed method, a well-focused radar image of moving target under low SNR condition can be obtained. Further, the estimation error caused by off-grid points as well as the unnecessary side lobe and main lobe values introduced by GRFT based ISAR imaging can be solved. Simulations are conducted to verify the effectiveness of the proposed method. © 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Inverse synthetic aperture radar

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Analysis of the Impact of Geosynchronous Satellite Motion on Vehicle-borne SAR Imaging

Analysis of the Impact of Geosynchronous Satellite Motion on...

引用

IEEE International Conference on Control Science and Systems Engineering (CCSSE)

作者： Tengyu Ma Zhiyang Chen Yuanhao Li Cheng Hu Beijing Key Laboratory of Real-Time Information Processing Technology of Embedded Beijing China School of Information and Electronic Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798331517199

ISBN: (纸本)9798331517205

Vehicle-borne synthetic aperture radar (SAR) has the advantages of flexible configuration, convenient deployment, and long endurance, making it an effective means for high-resolution imaging of space targets such as geosynchronous (GEO) satellites. Due to the long distance between the SAR platform and the GEO satellite, it requires extremely long synthetic aperture time to achieve high-resolution imaging. In this case, GEO satellite motion cannot be ignored, which causes defocusing in SAR imaging. To address this issue, this paper derived an accurate slant range model between the GEO satellite and the vehicle-borne SAR platform. Based on this, the impact of GEO satellite motion on SAR imaging and the impact boundary are analyzed. The accuracy of the slant range model and the impact boundary is verified through simulation.

关键词： Space vehicles Analytical models Satellites Accuracy Simulation High-resolution imaging radar polarimetry Polynomials Orbits Synthetic aperture radar

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An Enhancement Strategy for Wishart Classifier in Dual-Band and Dual-Pol SAR Classification

An Enhancement Strategy for Wishart Classifier in Dual-Band ...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yinghao Hu Yuanhao Li Zhiyang Chen Cheng Hu School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Polarimetric Synthetic Aperture radar (PolSAR) holds significant utility in classification through exploiting polarization information. However, many sensors are dual-pol, and their capabilities of classification are limited by the absence of polarization information. This paper focuses on dual-pol classification, proposing a two-step dual-band classification strategy for the Wishart classifier. The first step involves leveraging two bands to category the strong scattering pixels. And the second step entails optimizing the classification of weak scattering pixels using interferometry coherence. We use practical data from the C and L bands, employing Support Vector Machine (SVM) as a comparative experiment to validate the effectiveness of our strategy. The total accuracy and Kappa of the Wishart classifier finally increase by 3.92% and 0.05, respectively, indicating the effectiveness of our strategy. In addition, the second step also independently improves the performance of SVM, suggesting its versatility. This study can offer a novel perspective for dual-pol and multi-band classification.

关键词： Support vector machines Accuracy Statistical analysis Scattering Dual band Geoscience and remote sensing Coherence

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SAR Target Recognition Using Improved Monogenic-Based Feature Extraction Framework

SAR Target Recognition Using Improved Monogenic-Based Featur...

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2021 CIE International Conference on radar, radar 2021

作者： Li, Feng Yao, Weijun Li, Yang Chen, Wei Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center Beijing China School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-time Information Processing Technology Radar Research Lab Beijing China

ISBN: (纸本)9781665498142

Applying computer vision methods to synthetic aperture radar (SAR) image recognition is a research trend in recent years, and a series of valuable results have been achieved. In order to use machine learning classifiers for recognition, it is necessary to extract effective features, and most of these features are directly extracted based on grayscale SAR images. SAR data is usually rare, and more difficult to collect than optical images. Therefore, the problem of recognition using a small-size training set for SAR is more challenging to practical pattern recognition methods. The monogenic signal is an extended version of analytic signals in high-dimensional space which has attracted attention. In this paper, a new recognition framework which is based on feature dimensionality augmentation using combined multi-scale monogenic components and histogram of oriented gradient (HOG) feature is proposed. Proposed feature is named as MONO-HOG. This letter focuses on recognition under both standard operating condition (SOC) and small-sample scene. Experiments on moving and stationary target automatic recognition (MSTAR) data set show that our proposed framework has satisfying performance. © 2021 IEEE.

关键词： feature extraction monogenic signal small sample synthetic aperture radar (SAR) target recognition

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