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A tracking filter algorithm for migratory birds based on group situation constraints 2

A tracking filter algorithm for migratory birds based on gro...

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2nd IEEE International Conference on signal, Information and Data processing, ICSIDP 2024

作者： Li, Biao Wang, Rui Cai, Jiong Beijing Key Laboratory of Embedded Real-Time Information Processing Technology The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331515669

Aerial animal migration is closely related to human life, and radar is one of the most effective means of observing it. However, scanning radar has a low data rate, a short integration time, and lacks prior knowledge of target speed, while migratory animals exhibit high mobility. These factors make traditional tracking filter algorithms ineffective at predicting their movement. To address this issue, this paper proposes a tracking filter algorithm based on group dynamics constraints, leveraging the characteristic behavior of migratory animals flying in groups. First, the dynamic state of the group is calculated using the group as the research unit. Then, tracking filtering is performed under the constraint of group dynamics. Simulation and field data have verified the effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： Invertebrates

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Modeling and Verification of processing Engines in SAR Imaging SoC 2

Modeling and Verification of Processing Engines in SAR Imagi...

引用

2nd IEEE International Conference on signal, Information and Data processing, ICSIDP 2024

作者： Zhang, Zhihan Yang, Chao Xie, Yizhuang Li, Yongrui Zhang, Ao Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Radar Research Lab School of Information and Electoronics Beijing China Shanghai Institute of Satellite Engineering Shanghai China

ISBN: (纸本)9798331515669

With the development of real-time requirements for Synthetic Aperture Radar (SAR) imaging technology, SAR imaging processing SoC (System on Chip) has become an important research field at present. The subsystem for SAR imaging processing in the SAR imaging SoC needs to be fully verified during the development process. This article designs a UVM-based random verification environment, focusing on the verification requirements of key computing engines and interaction engines in the SAR imaging processing SoC. At the same time, a modeling method for computing engines and interaction engines is proposed, which can quickly and accurately describe the functions and behaviors of processing engines. Collaborating with the verification environment and the reference C model, the design under test achieves satisfactory coverage results. Consequently, the functionality of the processing engines within the subsystem can be comprehensively and effectively verified. The content studied in this article has certain value for the engineering verification implementation of SAR imaging algorithms in the SoC. © 2024 IEEE.

关键词： System-on-chip

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A method for enhancing infrared small target images under complex sky backgrounds 2

A method for enhancing infrared small target images under co...

引用

2nd IEEE International Conference on signal, Information and Data processing, ICSIDP 2024

作者： Xu, Yuhan Tang, Linbo Sun, Haotian Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Radar Research Lab School of Information and Electronics Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

ISBN: (纸本)9798331515669

Infrared target detection is currently widely used in both military and civilian fields. Due to the lower resolution of infrared images compared to visible light images and the lack of texture information in small targets, the quality of infrared small target images is often poor and not suitable for direct detection. Therefore, enhancing low-quality infrared small target images is a necessary step before object detection can be performed. This article presents a method for enhancing infrared images of small targets. The process begins with applying guided filtering to the original image. The resulting detail layer is then used as a guided image for a second guided filtering of the base layer, producing a second detail layer. Additionally, a novel approach to local contrast enhancement is introduced, which applies contrast enhancement specifically to this secondary detail layer. Experimental results has been demonstrated that the enhancement method proposed in this paper significantly improves the visibility of the target area while reducing background interference, without losing information about small targets. Additionally, this method outperforms other common techniques in terms of enhancement effect, as shown in comparative experiments. © 2024 IEEE.

关键词： Wiener filtering

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Radar Echo Modeling and Doppler Characteristic Fitting Method for UAVs Based on Multi-Scattering Point Model 2

Radar Echo Modeling and Doppler Characteristic Fitting Metho...

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2nd IEEE International Conference on signal, Information and Data processing, ICSIDP 2024

作者： Xu, Li'Ang Jiang, Qi Wang, Rui Zhang, Jichuan Jiao, Longxiang Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Institute of Technology Advanced Technology Research Institute Beijing China

ISBN: (纸本)9798331515669

The radar echo characteristics of unmanned aerial vehicles provide important information for identification. The radar echoes of various types of UAVs are modeled by the multi-scattering point modeling method. A Doppler characteristic modeling method for unmanned aerial vehicles based on the mixture Gaussian algorithm is proposed, in which the slow-time dimension data are extracted from the Range- Doppler and fitted by the mixture Gaussian algorithm. The parameters of the Gaussian distribution components representing the unmanned aerial vehicle's Doppler spread characteristics are obtained using the algorithm. The simulated radar echoes of the multi-rotor unmanned aerial vehicle and the flapping-wing unmanned aerial vehicle are used for analysis. The performance of the fitting is acceptable and the way in which various parameters affect the Doppler characteristics is discussed and concluded. © 2024 IEEE.

关键词： Gaussian distribution

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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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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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Lightweight NPU Method Based on Hyper-Threading Technology 2

Lightweight NPU Method Based on Hyper-Threading Technology

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2nd IEEE International Conference on signal, Information and Data processing, ICSIDP 2024

作者： Zhang, Ao Li, Yongrui Li, Wenlong Xie, Yizhuang Zhang, Zhihan Yang, Zhu Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Racobit Electronic Information Technology Co. Ltd Beijing China

ISBN: (纸本)9798331515669

With the rapid development of deep learning, the processing power of Neural processing Unit(NPU) continues to improve. However, this leads to an increasing scale of logical resources for NPU, making it challenging to deploy NPU on Field-Programmable Gate Array(FPGA). To address this issue and enable the deployment of high-computational-power NPU on FPGA, this paper proposes a lightweight NPU method based on hyper-threading technology, considering the characteristics of NPU hardware architecture. This method effectively reduces the logical resources usage of the NPU while ensuring only a minimal decrease in computational power, thus enabling successful FPGA deployment. The overall experiment is based on the Virtex UltraScale+ HBM VCU128 FPGA platform. After testing, it was found that the NPU, previously undeployable, could be successfully deployed after lightweight processing. The resource usage ratio of LUTs decreased by about 10%, and the computational power only decreased by 4%. In other words, by using this method, the scale of logical resource usage of the NPU was effectively reduced while ensuring a minimal decrease in computational power, improving the deployment situation of the NPU on FPGA. This approach has certain reference value and significance for the lightweight design of NPU. © 2024 IEEE.

关键词： Integrated circuit design

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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 ...

引用

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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SPACEBORNE SAR IMAGING SYSTEM WITH HIGH-PERFORMANCE POLYNOMIAL ENGINES

SPACEBORNE SAR IMAGING SYSTEM WITH HIGH-PERFORMANCE POLYNOMI...

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IET International Radar Conference 2023, IRC 2023

作者： Zhang, Zhihan Xu, Yinsheng Li, Yongrui Zhang, Ao 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 Shanghai Institute of Satellite Engineering Shanghai201109 China

ISBN: (纸本)9781839539954

In recent years, with the development of Synthetic Aperture Radar (SAR) technology, the required resolution and imaging bandwidth for spaceborne SAR have been continuously increasing, posing stringent requirements on real-time imaging systems. To address the issue of uneven computing power allocation in implementing the Chirp Scaling (CS) algorithm on an FPGA+DSP platform, a spaceborne SAR imaging system with high-performance polynomial engines was designed, which enables the mapping of the Taylor series-based Chirp Scaling algorithm onto a hardware platform. The system is implemented using Xilinx's XC7VX690T FPGA and TI's TMS320C6678 DSP. The high-performance polynomial engine is designed with a parallel pipeline structure, supporting computation of polynomial up to seventh order. It offers flexible configuration and high versatility. The spaceborne SAR imaging system with high-performance polynomial engines can achieve SAR imaging in 16K×16K resolution with a time of 15.64 seconds, meeting the requirements of real-time SAR imaging applications in large-scale data scenarios. © The Institution of Engineering & Technology 2023.

关键词： Radar imaging

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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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