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Model Switching Detection-Aided Adaptive Transition Probability Matrix IMM Algorithm 2

Model Switching Detection-Aided Adaptive Transition Probabil...

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

作者： Chen, Youwang Deng, Yunkai Tian, Weiming Radar Technology Research Institute Beijing Institute of Technology Beijing China Beijing Institute of Technology Yangtze Delta Region Academy Zhejiang Jiaxing China Beijing Institute of Technology Advanced Technology Research Institute Jinan China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Institute of Technology School of Information and Electronic Beijing China

ISBN: (纸本)9798331515669

In conventional interactive multiple model (IMM) algorithm, the fixed transition probability matrix results in slow model switching and low tracking accuracy. Existing adaptive transition probability IMM algorithms still suffer from large errors during model switching. This paper proposes a model switching detection-aided adaptive transition probability matrix IMM algorithm. To deal with model switch time delay, a transition probability correction function is established, capable of dynamically and rapidly adjusting the transition probabilities. And a model switching detection method is proposed to improve tracking accuracy during model switching. By the numerical simulation, the method's efficacy is substantiated and confirmed. © 2024 IEEE.

关键词： Adaptive Transition Probability Matrix IMM Model Switching Detection

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An Efficient Transposition Method for Spaceborne SAR Data Based on HBM

An Efficient Transposition Method for Spaceborne SAR Data Ba...

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

作者： Yongrui Li HuShan Lv Ming Xu Liang Chen Zhu Yang Yizhuang Xie Radar Technology Research Institute Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The onboard real-time processing of Spaceborne synthetic aperture radar (SAR) is of great significance for the rapid acquisition of surface information. As a key step of SAR imaging processing, the efficiency of data transposition directly affects the imaging quality and processing speed. In order to solve the problem of traditional double data rate synchronous dynamic random-access memory (DDR SDRAM) limiting the transposition speed, this paper proposes a SAR data transposition method based on high bandwidth memory (HBM). The method makes full use of the high bandwidth and multichannel characteristics of HBM, and significantly improves the transposition speed by optimising the data read/write process, providing a new solution for real-time imaging processing of satellite-borne SAR.

关键词： Limiting Spaceborne radar Imaging Bandwidth Radar imaging Real-time systems Radar polarimetry SDRAM Synthetic aperture radar Surface treatment

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Dual-Band HRRP Fusion Recognition via Wavelet Decomposition embedded Autoencoder 2

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

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

作者： Wang, Weijia Qi, Zhuchang Wang, Lue Yang, Wei Zhang, Liang Wang, Yanhua Xi'an Institute of Modern Control Technology Xi'an710065 China Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Radar Technology Research Institute 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

ISBN: (纸本)9798331515669

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. © 2024 IEEE.

关键词： Wavelet decomposition

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Dual-Band HRRP Recognition via Wavelet Packet Decomposition and Redundancy Reduction Model 2

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

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

作者： Yang, Wei Qi, Zhuchang Wu, Heke Li, Yang Zhang, Liang Wang, Yanhua Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou450000 China Xi'an Institute of Modern Control Technology Xi'an710065 China Beijing Key Laboratory of Embedded Real-time Information: Processing Technology Beijing100081 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China

ISBN: (纸本)9798331515669

High resolution range profiles (HRRP) is widely used in radar automatic target recognition. Due to aspect sensitivity, HRRP of different targets can be similar under certain aspects, making it difficult for recognition via traditional methods. One way to mitigate it is to increase the frequency dimension, utilizing the differences of target frequency response to extract inter-class discriminative information. Therefore, we propose a HRRP recognition model based on redundancy reduction criterion and wavelet packet decomposition. Specifically, we use deep wavelet packet decomposition to perform multi-level decomposition of dual-band HRRP and adaptively extract discriminative features of sub-wavelets;we use redundancy reduction layer to cluster intra-class features and expand inter-class features, thereby reducing feature heterogeneity. Furthermore, we propose a deep discriminative correlation analysis for joint analysis and transformation of dual-band features to fuse complementary information. Experimental results show that proposed method has excellent recognition performance. Additionally, analysis study also verify the effectiveness of each submodule. © 2024 IEEE.

关键词： Wavelet decomposition

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A TRACK ASSOCIATION ALGORITHM BASED ON MOTION DYNAMICS FEATURES IN ENTOMOLOGICAL RADAR

A TRACK ASSOCIATION ALGORITHM BASED ON MOTION DYNAMICS FEATU...

引用

IET International Radar Conference 2023, IRC 2023

作者： Li, Biao Liu, Hanzhe Zhang, Tianran Cai, Jiong Wang, Rui The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Beijing Institute of Electronic System Engineering Beijing100854 China

ISBN: (纸本)9781839539954

When tracking densely distributed targets such as insects, the traditional trajectory association algorithm exhibits poor correlation performance, leading to a decline in multi-target tracking efficiency. In this paper, based on the behavioral characteristics of insects gathering and co-orienting during aerial flight, a motion dynamic feature-assisted multi-target tracking method is proposed. Firstly, a three-dimensional biological distribution state matrix is constructed through spatial gridization. Then, a three-dimensional circular convolution is employed to estimate the target's motion dynamic feature, assisting in multi-target trajectory initialization. Finally, the improvement effect of the method on trajectory tracking accuracy is validated through simulations and migration insect data. © The Institution of Engineering & technology 2023.

关键词： Radar tracking

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Open Set Radar HRRP Recognition Using Confidence through Neural Weight Proximity 2

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

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

作者： Liu, Yichen Liu, Ping-An Zhang, Liang Wang, Yanhua Bao, Zengdi Xu, Yinhui Radar Technology Research Institute 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 Xi'an Modern Control Technology Research Institute Xi'an710065 China Zhengzhou Research Institute Beijing Institute of Technology Zhengzhou450000 China

ISBN: (纸本)9798331515669

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. © 2024 IEEE.

关键词： Radar target recognition

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AN MULTILAYER FUSION STRATEGY BASED ON IMPROVED YOLOV5 FOR SHIP DETECTION IN SAR IMAGES

AN MULTILAYER FUSION STRATEGY BASED ON IMPROVED YOLOV5 FOR S...

引用

IET International Radar Conference 2023, IRC 2023

作者： Chen, Fan Shi, Hao Zhao, Liangbo Mao, Yongfei Pan, Hongxin Chen, Liang Radar Research Lab Beijing Institute of Technology Beijing China General Department of Remote Sensing Satellites China Academy of Space Technology China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

Since entering the era of deep learning, the single-stage detection algorithm represented by YOLO has achieved some progress in the detection of ship targets by synthetic aperture radar (SAR). However, the accuracy of single-stage detection is lower than that of two-stage detection, especially for small target detection. To this end, this paper proposes an improved YOLOv5 detection method based on convolutional bolck attention module CBAM) and WPAN. At the same time, the focal efficient intersection over union (Focal EIoU) is introduced to optimize the calculation of bounding box regression loss. The experiment is verified on SAR ship detection dataset(SSDD). The results show that the proposed improved YOLOv5 algorithm can improve the false alarm and missing detection problems in multi-scale target detection, and improve the detection accuracy consequently. © The Institution of Engineering & technology 2023.

关键词： Synthetic aperture radar

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An Extremely Pipelined FPGA-based accelerator of All Adder Neural Networks for On-board Remote Sensing Scene Classification

An Extremely Pipelined FPGA-based accelerator of All Adder N...

引用

IEEE International Conference on Field-Programmable technology (FPT)

作者： Ning Zhang Shuo Ni Tingting Qiao Wenchao Liu He Chen Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

Directly completing remote sensing scene classification (RSSC) on space platforms can minimize latency and relieve data downlink burdens. The all adder neural network $( \mathrm{A}^{2}$NN) is a novel network for on-board RSSC with lower resource overhead than convolutional neural networks (CNNs). However, most of the existing FPGA-based accelerators are designed for CNNs and are not applicable to deploy $\mathrm{A}^{2}$NNs. In this paper, we propose an extremely pipelined FPGA-based accelerator of $\mathrm{A}^{2}$NNs and implement a VGGNet-11 backbone for on-board RSSC. In the proposed FPGA-based accelerator, an extremely pipelined processing engine (PE) suitable for accelerating the adder layer is designed. Each adder layer in the $\mathrm{A}^{2}$NN is mapped to a dedicated extremely pipelined PE, which achieves low-latency calculations. Besides, the entire parameters of the network are stored in block RAMs, and the intermediate data are cached on the FPGA chip, thereby eliminating external memory accesses and reducing power consumption. To evaluate the performance of the proposed extremely pipelined FPGA-based accelerator of $\mathrm{A}^{2}$NN, we implemented an $\mathrm{A}^{2}$NN-based RSCC model with the VGGNet-11 backbone on the Xilinx Virtex7 XC7VX690T FPGA by the proposed accelerator. The experimental results show that the proposed FPGA-based accelerator of $\mathrm{A}^{2}$NNs can achieve a throughput of 3.04 tera operations per second (TOPs) at 200 MHz while consuming 8.27 W.

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

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

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

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

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

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.

关键词： Target tracking Heuristic algorithms Dynamics Radar Filtering algorithms Radar tracking Prediction algorithms Birds information filters Radar signal processing

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An FPGA-GPU Heterogeneous System and Implementation for On-Board Remote Sensing Data processing

An FPGA-GPU Heterogeneous System and Implementation for On-B...

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IEEE International Conference on Field-Programmable technology (FPT)

作者： Tingting Qiao Yu Xie He Chen Yizhuang Xie Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

In resource-constrained space environments, energy-efficient and high-performance processors like Field- Programmable Gate Arrays (FPGAs) and Digital Signal Processors (DSPs) are widely utilized for on-board data processing. However, the variety of tasks in space missions, including remote sensing data pre-processing and processing, reveals the limitations of single processors. To address this, we suggest a heterogeneous system that combines FPGAs with embedded Graphics processing Unit (GPU) TX2 for efficient remote sensing data pre-processing and processing. Additionally, to overcome the challenges in data transfer between the FPGA and TX2, we utilized High-Level Synthesis (HLS) to design a dedicated IP for efficient data transfer between the FPGA and TX2 using PCIe protocol. This method enhances data transfer speeds and enables TX2 to manage address mapping in the Double Data Rate (DDR) memory. Our system successfully completes remote sensing data processing by integrating these elements.

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