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

共 205 条记录，以下是11-20 订阅

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realization of Remote Sensing Image Classification Algorithm Based on SOPC

Realization of Remote Sensing Image Classification Algorithm...

引用

IET International radar Conference 2023, IRC 2023

作者： Qi, Guijie Qiao, Tingting Xie, Yizhuang Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781839539954

Recently, Convolutional Neural Networks (CNNs) have made significant progress in the field of remote sensing image scene classification. However, commonly used CNN models are mainly deployed on GPU, which is bulky and power-hungry, making them unsuitable for customized requirements of on-board embedded applications. To meet the needs of on-board embedded applications, this study selects System On a Programmable Chip (SoPC), known for their low power consumption and compact size, as the processing platform for accelerating CNNs. Employing the knowledge distillation method, this paper uses the classical CNN scene classification algorithms to create lightweight models. These optimized models are then deployed on resource-constrained ZCU104 SoPC hardware platform. Experimental results demonstrate that the optimized lightweight models achieve image classification tasks with over 95% accuracy on SoPC hardware platform. Furthermore, due to minimal impact from quantization errors, the accuracy drop is less than 1%. Compared to GPU (GTX1650), the throughput on SoPC is increased by 96.43%. © The Institution of Engineering & technology 2023.

关键词： Convolutional neural networks

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Design of SAR Imaging System Based on MPSoC + FPGA with Optimization Strategies for Chirp Scaling Factor Computation

Design of SAR Imaging System Based on MPSoC + FPGA with Opti...

引用

IET International radar Conference 2023, IRC 2023

作者： Zhong, ZhiHui Qiao, TingTing Xie, YiZhuang Lv, HuShan Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781839539954

Space-based remote sensing involves complex SAR imaging hardware design. This complexity arises from varied imaging modes and a convoluted imaging process. With the rapid advancement of embedded systems and Field Programmable Gate Arrays (FPGA), underscores their flexibility, efficiency, and reconfigurability for SAR imaging hardware implementation. This paper introduces a comprehensive SAR imaging system based on Multi-Processor System on Chip (MPSoC) and FPGA architectures. The system supports a multi-modal SAR imaging processing platform with large granularity, encompassing orbital parameter pro-cessing, Chirp Scaling (CS) factor computation, data transposition, complex multiplication, and FFT operations. An effi-cient data link between the host computer, MPSoC, and FPGA is also established. To address the challenges of multiple parameters, intricate computational flow, and various computational types in Chirp Scaling factor generation, the system utilizes Cortex A53 and Cortex R5 processors for computation and optimization. The results indicate an average accelera-tion ratio of 25.296, a relative error of 1.12×10-4, and an absolute error of 5.88×10-6, demonstrating that the "MPSoC+FPGA"-based imaging system meets the demands of imaging processing. © The Institution of Engineering & technology 2023.

关键词： radar imaging

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Improved multiple hypothesis tracking using incomplete measurements for group targets 2

Improved multiple hypothesis tracking using incomplete measu...

引用

2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Ni, Na Jiang, Qi Hu, Cheng Beijing Institute of Technology Radar Technology Research Institute School of Information and Electronics Beijing China Beijing Key Laboratory of Real-Time Information Processing Technology of Embedded Beijing China Beijing Institute of Technology The Advanced Technology Research Institute Jinan China

ISBN: (纸本)9798331515669

Tracking targets within a group has received much attention in the radar field recently years. Group targets are usually closely spaced with similar velocity, making it hard to distinguish multiple targets. For precise target tracking, the tracking radar usually employs a narrow beam to achieve the high range-angular resolution. However, targets may enter and exit the narrow beam frequently during the observation, bringing difficulty for stable tracking. Moreover, it is also hard to distinguish different targets when their tracks intersect on the range-doppler plane. These kinds of incomplete measurements increase the difficulty of group targets' association and tracking. Therefore, this paper proposes an improved multiple hypothesis tracking method using incomplete measurements. The adaptive detection probability model and track intersecting probability are combined in the multiple hypothesis tracking framework to reduce false associations and improve tracking performance. Finally, simulation and experiment results verified the effectiveness of the proposed method. © 2024 IEEE.

关键词： Target tracking

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An Adaptive Estimation Method for Group Target Number Based on information Theoretic Criterion 2

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

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

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

ISBN: (纸本)9798331515669

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. © 2024 IEEE.

关键词： Monopulse radar

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Robust Adaptive OS-CFAR Detector for Maneuvering Group Target Based on Formation Prior Knowledge 2

Robust Adaptive OS-CFAR Detector for Maneuvering Group Targe...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Shi, Mengxin Jiang, Qi Wang, Rui Beijing Institute of Technology Radar Technology Research Institute School of Information and Electronics 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: (纸本)9798331515669

Detecting the individuals such like the bird flocks or a UAV swarm is a great challenge for radar systems. The lack of prior knowledge about the characteristics of dynamic group targets leads to the performance degradation of conventional detectors. In this study, we present a robust adaptive OS-CFAR detector for maneuvering group targets, leveraging prior knowledge of their formation. The group target's state is updated and predicted using the random matrix approach. The projection length of the group target in the radar beam direction is computed using the predicted information during the group target tracking process. Using the projection length, the number of reference units occupied by the group target within the reference window is determined, and this prior knowledge is applied to update the detection parameters of the OS-CFAR detector. The simulation result shows that the algorithm can update the detection parameters timely and avoid target missed detection. © 2024 IEEE.

关键词： radar tracking

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labeled Random Finite Sets-Based Group Target Tracking with Multi-Detection Model 2

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

引用

2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

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

ISBN: (纸本)9798331515669

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. © 2024 IEEE.

关键词： radar tracking

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research on SAR Image Matching Based on SAR-Harris Detector and Adaptive Locally-Affine Matching

Research on SAR Image Matching Based on SAR-Harris Detector ...

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IET International radar Conference 2023, IRC 2023

作者： Wu, Guanghui Shi, Hao Chen, Fan Pan, Hongxin Chen, Liang Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (纸本)9781839539954

Synthetic aperture radar (SAR) image matching is an essential step in SAR image processing. The synthetic aperture radar-scale invariant feature transform (SAR-SIFT), which combines the ratio of exponentially weighted averages (ROEWA) operator with multiscale Harris detection and employs Random Sample Consensus (RANSAC) for matching and filtering, has shown promising results. However, SAR-SIFT suffers from low matching efficiency in specific scenarios, and RANSAC exhibits strong randomness and potential problems of non-convergence. To address these problems, this paper introduces an approach that combines scale-space layers reduction in the SAR-Harris detector with the efficient Adaptive Locally-Affine Matching (AdaLAM) to enhance SAR image matching performance. The proposed SAR-SIFT-AdaLAM method is experimentally evaluated on two sets of different SAR images for matching, and the results indicate an improvement of approximately 25% in the number of matching points compared to the SAR-SIFT, along with a reduction of about 30% in matching time, demonstrating its effectiveness. © The Institution of Engineering & technology 2023.

关键词： Synthetic aperture radar

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Performance Analysis of Sub-object Partition Method for Linear Formation Targets

Performance Analysis of Sub-object Partition Method for Line...

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IET International radar Conference 2023, IRC 2023

作者： Xu, Li'ang Jiang, Qi Zhang, Jichuan Wang, Rui Hu, Cheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Shandong Jinan China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

The structure of bird formation is complex and variable. Traditional sub-object partition methods cannot match different types of linear formations, leading to a degradation of performance. Recently, a new sub-object partition method applicable to different formations has been proposed. However, the performance of the method has not been analyzed and verified thoroughly. This paper analyses the effectiveness of the proposed method. The statistical rules are proposed to evaluate the performance of the sub-object partition. A series of simulations are conducted using the method of controlling variates, with several important parameters of the method and formation being chosen and discussed separately. And the quantitative results of partition accuracy are demonstrated and the impact of each parameter on partition performance is discussed. Based on the conclusion above, an empirical method for parameter initialization is proposed, which guides the practical applications. © The Institution of Engineering & technology 2023.

关键词： CLUSTERING FORMATION TARGET GROUP TARGET SUB-OBJECT PARTITION

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An Adaptive OS-CFAR detector for Dynamic group targets

An Adaptive OS-CFAR detector for Dynamic group targets

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IET International radar Conference 2023, IRC 2023

作者： Shi, Mengxin Jiao, Longxiang Jiang, Qi Wang, Rui Hu, Cheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Shandong Jinan China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

Effective detection of group targets is of great significance to people's livelihood and national security. Flocks of birds, UAV swarms and other group targets are closely spaced in the radar beam and have high dynamics, which can easily lead to the mismatch of the traditional detector parameters and missed detection. Therefore, in this paper, to solve this problem, we established the detection model of the corresponding scene, derived the probability density function expression of the threshold, and proposed the OS-CFAR detector parameter correction method with the expectation of the maximum number of detected targets as the optimization criterion. Taking the OSPA distance as the detection performance index, the detection performance under different detection parameters is compared to verify the effectiveness of this algorithm, and the experiments show that the algorithm proposed in this paper can effectively reduce the target missed detection in the group targets scene. © The Institution of Engineering & technology 2023.

关键词： Target tracking

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ROBUST JOINT ASSOCIATION AND REGISTRATION UNDER LARGE SENSOR BIAS

ROBUST JOINT ASSOCIATION AND REGISTRATION UNDER LARGE SENSOR...

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IET International radar Conference 2023, IRC 2023

作者： Ni, Na Jiang, Qi Mao, Huafeng Zhang, Jichuan Wang, Rui Hu, Cheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Shandong Jinan China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

radar observation of group targets has recently received much attention in the flight mechanism research and many other applications. Group targets are usually closely spaced with lots of missed detections and false alarms, making it difficult for spatial reconstruction. Multisensor systems make use of data from multiple radars to provide more accurate measurements and robust tracks than a single radar, playing an important role in the group targets observation. In the data fusion processes, multisensor measurements are associated after transformed into a global coordinate system. However, large sensor bias makes it difficult to associate measurements of closely spaced targets given outliers, and nonideal association further increase the sensor bias estimation error. In this paper, we proposed a robust joint association and registration method to simultaneously acquire sensor bias estimates and association results. A nonlinear least median of squares estimator is used to achieve better accuracy and robustness of sensor bias estimation. Simulation results show the better bias estimation and association performance of the proposed method compared to other algorithms. © The Institution of Engineering & technology 2023.

关键词： radar measurement

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