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Randomized consensus based distributed kalman filtering over wireless sensor networks

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arXiv 2018年

作者： Qin, Jiahu Wang, Jie Shi, Ling Kang, Yu Department of Automation University of Science and Technology of China Hefei230027 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Automation State Key Laboratory of Fire Science Institute of Advanced Technology University of Science and Technology of China Hefei230027 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China

This paper is concerned with developing a novel distributed Kalman filtering algorithm over wireless sensor networks based on randomized consensus strategy. Compared with centralized algorithm, distributed filtering techniques require less computation per sensor and lead to more robust estimation since they simply use the information from the neighboring nodes in the network. However, poor local sensor estimation caused by limited observability and network topology changes which interfere the global consensus are challenging issues. Motivated by this observation, we propose a novel randomized gossip based distributed Kalman filtering algorithm. information exchange and computation in the proposed algorithm can be carried out in an arbitrarily connected network of nodes. In addition, the computational burden can be distributed for a sensor which communicates with a stochastically selected neighbor at each clock step under schemes of gossip algorithm. In this case, the error covariance matrix changes stochastically at every clock step, thus the convergence is considered in a probabilistic sense. We provide the mean square convergence analysis of the proposed algorithm. Under a sufficient condition, we show that the proposed algorithm is quite appealing as it achieves better mean square error performance theoretically than the noncooperative decentralized Kalman filtering algorithm. Besides, considering the limited computation, communication and energy resources in the wireless sensor networks, we propose an optimization problem which minimizes the average expected state estimation error based on the proposed algorithm. To solve the proposed problem efficiently, we transform it into a convex optimization problem. And a sub-optimal solution is attained. Examples and simulations are provided to illustrate the theoretical results. Copyright © 2018, The Authors. All rights reserved.

关键词： Kalman filters

SAR image classification based on spiking neural network through spike-time dependent plasticity and gradient descent

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arXiv 2021年

作者： Chen, Jiankun Qiu, Xiaolan Ding, Chibiao Wu, Yirong National Key Laboratory of Science and Technology on Microwave Imaging Institute of Electronics Aerospace Information Research Institute Beijing10090 China The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing10090 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Suzhou Research Institute Aerospace Information Research Institute Chinese Academy of Sciences Suzhou215123 China The University of Chinese Academy of Sciences Beijing10049 China

At present, the Synthetic Aperture Radar (SAR) image classification method based on convolution neural network (CNN) has faced some problems such as poor noise resistance and generalization ability. Spiking neural network (SNN) is one of the core components of brain-like intelligence and has good application prospects. This article constructs a complete SAR image classifier based on unsupervised and supervised learning of SNN by using spike sequences with complex spatio-temporal information. We firstly expound the spiking neuron model, the receptive field of SNN, and the construction of spike sequence. Then we put forward an unsupervised learning algorithm based on STDP and a supervised learning algorithm based on gradient descent. The average classification accuracy of single layer and bilayer unsupervised learning SNN in three categories images on MSTAR dataset is 80.8% and 85.1%, respectively. Furthermore, the convergent output spike sequences of unsupervised learning can be used as teaching signals. Based on the TensorFlow framework, a single layer supervised learning SNN is built from the bottom, and the classification accuracy reaches 90.05%. By comparing noise resistance and model parameters between SNNs and CNNs, the effectiveness and outstanding advantages of SNN are verified. Code to reproduce our experiments is available at https://***/Jiankun-chen/Supervised-SNN-with-GD. © 2021, CC BY.

关键词： Gradient methods

Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models

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arXiv 2023年

作者： Qi, Xiyu Wu, Yifan Mao, Yongqiang Zhang, Wenhui Zhang, Yidan Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China

The Segment Anything Model (SAM) exhibits remarkable versatility and zero-shot learning abilities, owing largely to its extensive training data (SA-1B). Recognizing SAM's dependency on manual guidance given its category-agnostic nature, we identified unexplored potential within few-shot semantic segmentation tasks for remote sensing imagery. This research introduces a structured framework designed for the automation of few-shot semantic segmentation. It utilizes the SAM model and facilitates a more efficient generation of semantically discernible segmentation outcomes. Central to our methodology is a novel automatic prompt learning approach, leveraging prior guided mask to produce coarse pixel-wise prompts for SAM. Extensive experiments on the DLRSD datasets underlines the superiority of our approach, outperforming other available few-shot methodologies. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

Multi-dimensional Graph Linear Canonical Transform

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arXiv 2024年

作者： Li, Na Zhang, Zhichao Han, Jie Chen, Yunjie Cao, Chunzheng School of Mathematics and Statistics Center for Applied Mathematics of Jiangsu Province Jiangsu International Joint Laboratory on System Modeling and Data Analysis Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Key Laboratory of Computational Science and Application of Hainan Province Haikou571158 China School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Nanjing210044 China

Many multi-dimensional (M-D) graph signals appear in the real world, such as digital images, sensor network measurements and temperature records from weather observation stations. It is a key challenge to design a transform method for processing these graph M-D signals in the linear canonical transform domain. This paper proposes the two-dimensional graph linear canonical transform based on the central discrete dilated Hermite function (2-D CDDHFs-GLCT) and the two-dimensional graph linear canonical transform based on chirp multiplication-chirp convolution-chirp multiplication decomposition (2-D CM-CC-CM-GLCT). Then, extending 2-D CDDHFs-GLCT and 2-D CM-CC-CM-GLCT to M-D CDDHFs-GLCT and M-D CM-CC-CM-GLCT. In terms of the computational complexity, additivity and reversibility, M-D CDDHFs-GLCT and M-D CM-CC-CM-GLCT are compared. Theoretical analysis shows that the computational complexity of M-D CM-CC-CM-GLCT algorithm is obviously reduced. Simulation results indicate that M-D CM-CC-CM-GLCT achieves comparable additivity to M-D CDDHFs-GLCT, while M-D CM-CC-CM-GLCT exhibits better reversibility. Finally, M-D GLCT is applied to data compression to show its application advantages. The experimental results reflect the superiority of M-D GLCT in the algorithm design and implementation of data compression. Copyright © 2024, The Authors. All rights reserved.

关键词： Chirp modulation

TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

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arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Wang, Yue Gao, Silin Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA algorithm are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

A new method for image understanding and retrieval using text-mined knowledge

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Lecture Notes in Computer Science (including subseries Lectu...

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8933卷 684-694页

作者： Tian, Jing Huang, Tinglei Huang, Y. Zhang, Z. Guo, Zhi Fu, Kun Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences No.19 North 4th Ring Road West Haidian District Beijing100190 China University of Chinese Academy of Sciences No.19A Yuquan Road Shijingshan District Beijing100049 China

Existing approaches for image annotation generally demand training data with exact image labels or human-generated tags, which are often difficult to obtain. In this paper we present a novel model that utilizes the rich surrounding text of images to perform image annotation. Our work makes two main contributions. First, by integrating the state-of-the-art text analysis methods, words that describe the salient objects in images are extracted. Second, a new probabilistic topic model is built to jointly model image features, extracted words and surrounding text. Our model is demonstrated to be flexible enough to handle multi-modal features and provide better annotation prediction performance than the baseline model proposed in previous research. © Springer International Publishing Switzerland 2014.

关键词： Image annotation

Some Reflections and Simulations on the Resolution and Focusing Approach of Microwave Photonic SAR with an Across-Band Bandwidth

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Some Reflections and Simulations on the Resolution and Focus...

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Min Chen Xiaolan Qiu Xuejiao Wen Hui Chen Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Chinese Academy of Sciences The Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Beijing China The School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Suzhou Aerospace Information Research Institute Suzhou China State key Laboratory of Millimeter Waves Southeast University Nanjing China

Microwave photonic (MWP) SAR technology can realize large bandwidth even across multiple wavebands and therefore improves the imaging resolution significantly. However, when targets are illuminated by the electromagnetic wave with such an across-band bandwidth, their scattering characteristics will change with signal frequency, which is not taken into consideration for conventional SAR imaging. In this paper, in order to investigate the resolution and optimize the imaging of MWP SAR, we first design several typical structures and conduct electromagnetic simulations on them, from which, we obtain the variation of scattering characteristics within an across-band bandwidth. Then, we use different focusing approaches for one-dimensional imaging processing and adopt different evaluation indicators to evaluate the performance of these methods comprehensively. Finally, with detailed analysis, we give suggestions on the selection of compression methods for different structures at different SNRs.

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Vision-Language Consistency Guided Multi-modal Prompt Learning for Blind AI Generated Image Quality Assessment

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arXiv 2024年

作者： Fu, Jun Zhou, Wei Jiang, Qiuping Liu, Hantao Zhai, Guangtao The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China The School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom The School of Information Science and Engineering Ningbo University Ningbo315211 China The Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China

Recently, textual prompt tuning has shown inspirational performance in adapting Contrastive Language-Image Pre-training (CLIP) models to natural image quality assessment. However, such uni-modal prompt learning method only tunes the language branch of CLIP models. This is not enough for adapting CLIP models to AI generated image quality assessment (AGIQA) since AGIs visually differ from natural images. In addition, the consistency between AGIs and user input text prompts, which correlates with the perceptual quality of AGIs, is not investigated to guide AGIQA. In this letter, we propose vision-language consistency guided multi-modal prompt learning for blind AGIQA, dubbed CLIP-AGIQA. Specifically, we introduce learnable textual and visual prompts in language and vision branches of CLIP models, respectively. Moreover, we design a text-to-image alignment quality prediction task, whose learned vision-language consistency knowledge is used to guide the optimization of the above multi-modal prompts. Experimental results on two public AGIQA datasets demonstrate that the proposed method outperforms state-of-the-art quality assessment models. The source code is available at https://***/JunFu1995/CLIP-AGIQA. © 2024, CC BY-NC-ND.

关键词： Learning systems

Satellite-observed changes in terrestrial vegetation growth trends across the Asia-Pacific region associated with land cover and climate from 1982 to 2011

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International Journal of Digital Earth 2016年第11期9卷 1055-1076页

作者： Baozhang Chen Guang Xu Nicholas C.Coops Philippe Ciais Ranga B.Myneni Key Laboratory of Soil and Water Conservation and Desertification Combating Ministry of EducationBeijing Forestry UniversityBeijingPeople’s Republic of China School of Environment Science and Spatial Informatics China University of Mining and TechnologyXuzhouPeople’s Republic of China Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application NanjingPeople’s Republic of China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingPeople’s Republic of China University of Chinese Academy of Sciences BeijingPeople’s Republic of China Department of Forest Resource Management University of British ColumbiaVancouverBCCanada Laboratoire des Sciences du Climat et de l’Environnement UnitéMixte de Recherche Commissariatàl’Energie AtomiqueCentre National de la Recherche ScientifiqueUniversitéde Versailles Saint-Quentin-en-YvelinesGif-sur-YvetteFrance Department of Earth and Environment Boston UniversityBostonMAUSA

The Asia-Pacific(AP)region has experienced faster warming than the global average in recent decades and has experienced more climate extremes,however little is known about the response of vegetation growth to these *** updated Global Inventory Modeling and Mapping Studies third-generation global satellite Advanced Very High Resolution Radiometer Normalized Difference Vegetation Index dataset and gridded reanalysis climate data were used to investigate the spatiotemporal changes in both trends of vegetation dynamic indicators and climatic *** then further analyzed their relations associated with land cover across the AP *** main findings are threefold:(1)at continental scales the AP region overall experienced a gradual and significant increasing trend in vegetation growth during the last three decades,and this NDVI trend corresponded with an insignificant increasing trend in temperature;(2)vegetation growth was negatively and significantly correlated with the Pacific Decadal Oscillation index and the El Niño/Southern Oscillation(ENSO)in AP;and(3)at pixel scales,except for Australia,both vegetation growth and air temperature significantly increased in the majority of study regions and vegetation growth spatially correlated with temperature;In Australia and other water-limited regions vegetation growth positively correlated with precipitation.

关键词： Vegetation dynamics NDVI climate change land cover Asia-Pacific region