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A novel TomoSAR imaging method with few observations based on nested array

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arXiv 2022年

作者： Jiang, Pengyu Zhang, Zhe Zhang, Bingchen Xu, Zhongqiu Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Suzhou Aerospace Information Research Institute Suzhou215000 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou21500 China

Synthetic aperture radar tomography (TomoSAR) baseline optimization technique is capable of reducing system complexity and improving the temporal coherence of data, which has become an important research in the field of TomoSAR. In this paper, we propose a nested TomoSAR technique, which introduces the nested array into TomoSAR as the baseline configuration. This technique obtains uniform and continuous difference co-array through nested array to increase the degrees of freedom (DoF) of the system and expands the virtual aperture along the elevation direction. In order to make full use of the difference co-array, covariance matrix of the echo needs to be obtained. Therefore, we propose a TomoSAR sparse reconstruction algorithm based on nested array, which uses adaptive covariance matrix estimation to improve the estimation performance in complex scenes. We demonstrate the effectiveness of the proposed method through simulated and real data experiments. Compared with traditional TomoSAR and coprime TomoSAR, the imaging results of our proposed method have a better anti-noise performance and retain more image information. Copyright © 2022, The Authors. All rights reserved.

关键词： Covariance matrix

A parallel optimization model for massive data stream application

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Ruan Jian Xue Bao/Journal of Software 2009年第SUPPL. 1期20卷 23-33页

作者： Sun, Xiao-Juan Sun, Ning-Hui Lei, Bin Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Institute of Electronics The Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Computer System and Architecture Institute of Computing Technology The Chinese Academy of Sciences Beijing 100190 China

While computing is entering a new phase in which CPU improvements are driven by the addition of multiple cores on a single chip, rather than higher frequencies. Parallel processing on these systems is in a primitive stage, and requires the explicit use and knowledge of underlying thread architecture. Based on the features of massive data stream application, this paper proposes three-level pipelining programming model of multithreading system, which realizes the new synchronization mechanism with no contention of shared structures and is capable to provide differential service for data streams. Then the paper applies the new model to remote sensing information processing system and backbone network intrusion detection system, and evaluates the improved system on several multicore platforms. In performance analysis, the optimized effects of backbone network intrusion detection system are evaluated in several aspects of throughput scalability on both SPARC T1 and x86 platforms, the impacts of different multithreading mapping methods on throughput, and the comparison of response time and service quality before and after optimization. The experimental results show that the system throughput has good scalability on both platforms, the values of response time are greatly improved and the prioritized streams achieve better response time with the differential service mechanism. © by Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Metadata

Periodicity in Hedge-myopic system and an asymmetric NE-solving paradigm for two-player zero-sum games

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arXiv 2024年

作者： Guo, Xinxiang Mu, Yifen Yang, Xiaoguang School of Mathematical Sciences University of Chinese Academy of Sciences China The Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences China Key Laboratory of Management Decision and Information System Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences China

In this paper, we consider the n×n two-payer zero-sum repeated game in which one player (player X) employs the popular Hedge (also called multiplicative weights update) learning algorithm while the other player (player Y) adopts the myopic best response. We investigate the dynamics of such Hedge-myopic system by defining a metric Q(xt), which measures the distance between the stage strategy xt and Nash Equilibrium (NE) strategy of player X. We analyze the trend of Q(xt) and prove that it is bounded and can only take finite values on the evolutionary path when the payoff matrix is rational and the game has an interior NE. Based on this, we prove that the stage strategy sequence of both players are periodic after finite stages and the time-averaged strategy of player Y within one period is an exact NE strategy. Accordingly, we propose an asymmetric paradigm for solving two-player zero-sum games. For the special game with rational payoff matrix and an interior NE, the paradigm can output the precise NE strategy;for any general games we prove that the time-averaged strategy can converge to an approximate NE. In comparison to the NE-solving method via Hedge self-play, this HBR paradigm exhibits faster computation/convergence,better stability and can attain precise NE convergence in most real *** Codes 91A05, 91A25, 91A68 Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

Synaptic learning with augmented spikes

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arXiv 2020年

作者： Yu, Qiang Song, Shiming Ma, Chenxiang Pan, Linqiang Tan, Kay Chen Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Key Laboratory of Image Information Processing and Intelligent Control Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China The Department of Computer Science City University of Hong Kong Hong Kong

Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones. With a more brain-like processing paradigm, spiking neurons are more promising for improvements on efficiency and computational capability. They extend the computation of traditional neurons with an additional dimension of time carried by all-or-nothing spikes. Could one benefit from both the accuracy of analog values and the time-processing capability of spikes? In this paper, we introduce a concept of augmented spikes to carry complementary information with spike coefficients in addition to spike latencies. New augmented spiking neuron model and synaptic learning rules are proposed to process and learn patterns of augmented spikes. We provide systematic insight into the properties and characteristics of our methods, including classification of augmented spike patterns, learning capacity, construction of causality, feature detection, robustness and applicability to practical tasks such as acoustic and visual pattern recognition. The remarkable results highlight the effectiveness and potential merits of our methods. Importantly, our augmented approaches are versatile and can be easily generalized to other spike-based systems, contributing to a potential development for them including neuromorphic computing. Copyright © 2020, The Authors. All rights reserved.

关键词： Neurons

End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning

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arXiv 2024年

作者： Li, Yueyuan Jiang, Mingyang Zhang, Songan Yuan, Wei Wang, Chunxiang Yang, Ming The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China The Innovation Center of Intelligent Connected Vehicles Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China

Dynamic and interactive traffic scenarios pose significant challenges for autonomous driving systems. Reinforcement learning (RL) offers a promising approach by enabling the exploration of driving policies beyond the constraints of pre-collected datasets and predefined conditions, particularly in complex environments. However, a critical challenge lies in effectively extracting spatial and temporal features from sequences of high-dimensional, multi-modal observations while minimizing the accumulation of errors over time. Additionally, efficiently guiding large-scale RL models to converge on optimal driving policies without frequent failures during the training process remains tricky. We propose an end-to-end model-based RL algorithm named Ramble to address these issues. Ramble processes multi-view RGB images and LiDAR point clouds into low-dimensional latent features to capture the context of traffic scenarios at each time step. A transformer-based architecture is then employed to model temporal dependencies and predict future states. By learning a dynamics model of the environment, Ramble can foresee upcoming traffic events and make more informed, strategic decisions. Our implementation demonstrates that prior experience in feature extraction and decision-making plays a pivotal role in accelerating the convergence of RL models toward optimal driving policies. Ramble achieves state-of-the-art performance regarding route completion rate and driving score on the CARLA Leaderboard 2.0, showcasing its effectiveness in managing complex and dynamic traffic situations. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

A selection method of frequency combination in MF-INSAR system

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A selection method of frequency combination in MF-INSAR syst...

IET International Radar Conference 2015

作者： Yalin Qi Qi Zhang Wenfu Yang Shuangjing Yang Tiandong Liu China academy of Launch vehicle technology Beijing 100076 China Beijing Institute of Spacecraft system Engineering CAST Beijing 100094 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Radar Research Lab. School of Information and Electronics Beijing Institute of Technology Beijing 100081 China

ISBN: (纸本)9781510822023

As an extension of the Interferometric Synthetic aperture radar (InSAR), the multi-frequency InSAR can realize high-accuracy elevation extraction of the steep terrain. Though the maximum likelihood estimation (MLE) is widely used in the signal processing of multi-frequency InSAR, the performance of this method can be influenced by the selection of frequency combination. At the beginning, this paper analyses the elevation estimated effectiveness of different frequency combinations from the aspect of Cramer-Rao Lower Bound (CRLB). Then, it calculates the ambiguity cycle as a further filter for the optimal frequency combination. Finally, this paper discusses the adaptability to the phase noise with the help of Monte Carlo simulation strategy. In consideration of these three aspects, a selection method of frequency combination is proposed, and further simulation results are provided in quantity. Relevant researches can be used in the design of multi-frequency InSAR system.

关键词： CRLB MLE Multi-frequency InSAR ambiguity cycle frequency combination phase noise

Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective

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arXiv 2022年

作者： Zhu, Yao Chen, Yuefeng Li, Xiaodan Chen, Kejiang He, Yuan Tian, Xiang Zheng, Bolun Chen, Yaowu Huang, Qingming The Zhejiang University Hangzhou310013 China The Security Department of Alibaba Group China The CAS Key Laboratory of Electro-Magnetic Space Information University of Science and Technology of China China The Hangzhou Dianzi University China Zhejiang Provincial Key Laboratory for Network Multimedia Technologies China The Zhejiang University Zhejiang Provincial Key Laboratory for Network Multimedia Technologies China The Zhejiang University Zhejiang University Embedded System Engineering Research Center Ministry of Education of China China The University of Chinese Academy of Sciences China The Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences China

Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and then attack the unknown target model successfully, which brings a severe threat to DNNs. The exact underlying reasons for the transferability are still not completely understood. Previous work mostly explores the causes from the model perspective, e.g., decision boundary, model architecture, and model capacity. adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and then attack the unknown target model successfully, which brings a severe threat to DNNs. The exact underlying reasons for the transferability are still not completely understood. Previous work mostly explores the causes from the model perspective, e.g., decision boundary, model architecture, and model capacity. Here, we investigate the transferability from the data distribution perspective and hypothesize that pushing the image away from its original distribution can enhance the adversarial transferability. To be specific, moving the image out of its original distribution makes different models hardly classify the image correctly, which benefits the untargeted attack, and dragging the image into the target distribution misleads the models to classify the image as the target class, which benefits the targeted attack. Towards this end, we propose a novel method that crafts adversarial examples by manipulating the distribution of the image. We conduct comprehensive transferable attacks against multiple DNNs to demonstrate the effectiveness of the proposed method. Our method can significantly improve the transferability of the crafted attacks and achieves state-of-the-art performance in both untargeted and targeted scenarios, surpassing the previous best method by up to 40% in some cases. In summary, our work provides new in

关键词： Deep neural networks

Truncated Beam Sweeping for Spatial Covariance Matrix Reconstruction in Hybrid Massive MIMO

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arXiv 2022年

作者： Liu, Yinsheng Duan, Hongtao Liao, Xi State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-spped Railway System China Beijing100037 China Key Laboratory of Complex Environmental Communications School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China

Spatial covariance matrix (SCM) is essential in many applications of multi-antenna systems such as massive multiple-input multiple-output (MIMO). For massive MIMO operating at millimeter-wave bands, hybrid analog-digital structure has been adopted to reduce the cost of radio frequency (RF) chains. In this situation, signals received at the antennas are unavailable to the digital receiver, and as a consequence, traditional sample average approach cannot be used for SCM reconstruction in hybrid massive MIMO. To address this issue, beam sweeping algorithm (BSA), which can reconstruct SCM effectively in hybrid massive MIMO, has been proposed in our previous work. In this paper, a truncated BSA is further proposed for SCM reconstruction by taking into account the patterns of antenna elements in the array. Due to the directive antenna pattern, sweeping results corresponding to predetermined direction-of-angles (DOA) far from the normal direction are small and thus can be replaced by predetermined constants. At the cost of negligible performance reduction, SCM can be reconstructed efficiently by sweeping only the predetermined DOAs that are close to the normal direction. In this way, BSA can be conducted much faster than its traditional counterpart. Insightful analysis will be also included to show the impact of truncation on the performance. © 2022, CC BY.

关键词： Millimeter waves

Design of achromaticsurface microstructure for near-eye display with diffractive waveguide

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arXiv 2017年

作者： Xiao, Jiasheng Liu, Juan Han, Jian Wang, Yongtian China Beijing Engineering Research Center for Mixed Reality and Advanced Display School of Optoelectronics Beijing Institute of Technology Beijing100081 China Key Laboratory of Information System and Technology Beijing Institute of Control and Electronic Technology Beijing100038 China

Dispersion problem has always constrained the development of see-Through near-eye displays with diffractive waveguide. Here, we propose a design method and optical systems for achromatic surface microstructure composed with a triple-carved-sub-grating. The triple-carved-sub-grating with specified period is designed based on rigorous coupled wave theory, corresponding to Red, Green and Blue (RGB) wavelengths, respectively, with diffraction efficiencies of ±1st orders reach 47.5% under TE polarization. The surface microstructure with period of 18.9 is realized by integrating the three sub-gratings together, and it is verified numerically that the diffractive angle of RGB wavelengths is 35° and the crosstalk is less than 4.5% under the normal incidence of TE polarization, which imply the surface microstructure diffracts the certain RGB wavelengths achromatically. Via proper duplication it could be utilized as the combining optics of diffractive-waveguide near-eye display and head-up display because of its light and compact features. m. Copyright © 2017, The Authors. All rights reserved.

关键词： Diffraction