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检索条件"任意字段=International Conference on Estimation Detection and Information Fusion ICEDIF"

共 1409 条记录，以下是41-50 订阅

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Fault detection and Noise Variance Identifier with Cooperation Fault-torlerance for Multisensor System

Fault Detection and Noise Variance Identifier with Cooperati...

引用

international conference on estimation detection and information fusion icedif

作者： Wang, Xin Sun, Shuli Shi, Ying Heilongjiang Univ Dept Automat Harbin 15000 Heilongjiang Pr Peoples R China

ISBN: (纸本)9781479964185

For the multisensor linear discrete time-invariant stochastic system with unknown noise variances, the new measurement system is constructed by using matrix pseudo-inverse method, which can yield many groups of new measurement sequences by cooperating work. Furthermore, the statistics characteristics of the new measurement sequences are analyzed to determine whether the sensors are faulting or not. The self-tuning cooperation fault-torlerance algorithm are presented. Meanwhile, the linear matrix equations are constructed about measurement noise variances. Then the measurement noise variances are found by solving the equations. A simulation example for a tracking system with 3-sensor shows its effectiveness.

关键词： Self-tuning Cooperation fault-torlerance Linear matrix equations

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Frequency estimation with Missing Measurements

Frequency Estimation with Missing Measurements

引用

international conference on estimation detection and information fusion icedif

作者： Ding, Dongyan Liu, Hongqing Li, Yong Zhou, Yi Chongqing Univ Posts & Telecommun Chongqing Key Lab Mobile Commun Technol Chongqing Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Peoples R China

ISBN: (纸本)9781479964185

The frequency estimation problem is studied in this work in the presence of missing measurements. The approach developed in this work is mainly inspired by sparse signal theory. To find a sparse representation of frequency estimation problem, a DFT-like matrix is created in which the frequency sparsity is discovered. The missing measurements are modeled by a sparse representation as well where missing samples are set to be zeros. Based on this model, the missing pattern represented by a vector in this work is indeed sparse since it only contains zeros and ones. Therefore, by exploring the sparsity of both frequency and missing petters, a joint estimation is devised under optimization framework. To solve that optimization problem, a two-step process is proposed as well. Numerical studies demonstrate that the joint estimation offers precise and consistent results.

关键词： Frequency estimation missing measurements sparsity joint estimation

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Knowledge-aided Shrinkage Interference Covariance Matrix estimation in STAP

Knowledge-aided Shrinkage Interference Covariance Matrix Est...

引用

international conference on estimation detection and information fusion icedif

作者： Han, Sudan Fan, Chongyi Huang, Xiaotao Natl Univ Def Technol Coll Elect Sci & Engn Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781479964185

This paper proposes a knowledge-aided shrinkage interference covariance matrix estimation approach in space time adaptive processing (STAP) which exploits the prior interference covariance matrix based on clutter model as the shrinkage target matrix. The oracle shrinkage coefficient under Gaussian assumption is derived and the estimated consistent shrinkage coefficient is also provided since the true interference covariance matrix in the oracle coefficient expression is usually unknown. With the comparison to the identity matrix and scaled identity matrix which are most commonly used as the shrinkage target matrix, the superiority of using prior knowledge is validated using simulated data. To evaluate the sensibility of the proposed method to the prior knowledge error, performance employing inaccurate knowledge is also considered in this paper. Simulation results show that the proposed method can significantly improve the STAP performance, even when the prior knowledge has some errors.

关键词： knowledge-aided interference covariance matrix estimation shrinkage approach STAP

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Research of SQL injection attack and prevention technology

Research of SQL injection attack and prevention technology

引用

international conference on estimation, detection and information fusion, icedif 2015

作者： Qian, Li Zhu, Zhenyuan Hu, Jun Liu, Shuying Institute of Information Engineering of Anhui Xinhua University Hefei China University of Science and Technology of China Hefei China

ISBN: (纸本)9781479964178

SQL injection attack is one of the most serious security vulnerabilities in Web application system, most of these vulnerabilities are caused by lack of input validation and SQL parameters use. Typical SQL injection attack and prevention technologies are introduced in the paper. The detecting methods not only validate user input, but also use type-safe SQL parameters. SQL injection defense model is established according to the detection processes, which is effective against SQL injection vulnerabilities. © 2015 IEEE.

关键词： Computer crime

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Target Direction-of-Arrival estimation Using Nested Frequency Diverse Array

Target Direction-of-Arrival Estimation Using Nested Frequenc...

引用

international conference on estimation detection and information fusion icedif

作者： Zhu, Chenglong Wang, Wen-Qin Chen, Hui Shao, Huaizong Univ Elect Sci & Technol China Sch Commun & Informat Engn Chengdu Peoples R China

ISBN: (纸本)9781479964185

This paper proposes a receiving nested frequency diverse array (FDA) design scheme. The essence of the proposed technique is to construct a new array structure by systematically nesting two or more uniform linear FDA. Using second-order statistics of the received data, it is capable of providing a significant increasing degrees-of-freedom. The frequency increment across the array results in a scan angle that varies with range and provides resistance to range-dependent interference. The improvement offered by the proposed method as compared to traditional FDA are demonstrated by extensive simulation through analyzing the corresponding beampattern and direction-of-arrival estimation performance.

关键词： Frequency diverse array (FDA) FDA radar nested array range-dependent direction-of-arrival nested FDA

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DOA estimation under Sensor Gain and Phase Uncertainties

DOA Estimation under Sensor Gain and Phase Uncertainties

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international conference on estimation detection and information fusion icedif

作者： Zhao, Luming Liu, Hongqing Li, Yong Zhou, Yi Chongqing Univ Posts & Telecommun Chongqing Key Lab Mobile Commun Technol Chongqing Peoples R China Chongqing Univ Posts & Telecommun Sch Informat & Commun Engn Chongqing Peoples R China

ISBN: (纸本)9781479964185

This work addresses the problem of direction-ofarrival (DOA) estimation in the presence of sensor's gain and phase uncertainties. The main motivation in our work is to explore the signal sparse property. The DOA sparsity is relatively easy to be discovered since the spectrum of signal is sparse in spatial domain. One existing finding on uncertainty matrix is it is a sparse matrix since it only is diagonal. To combat the gain and phase uncertainties, a direct estimate of uncertainty matrix is developed by utilizing its sparse property. By exploring the sparsity of both the DOAs and uncertainties of gain and phase, a two-step iterative process is proposed to achieve a joint estimation of DOAs and gain and phase uncertainties. Numerical studies are presented to demonstrate the effectiveness of the joint estimation.

关键词： DOA estimation gain and phase uncertainty sparsity joint estimation

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1st international Symposium on Intelligent Technology for Future Transportation, ITFT 2024

1st International Symposium on Intelligent Technology for Fu...

引用

1st international Symposium on Intelligent Technology for Future Transportation, ITFT 2024

ISBN: (纸本)9783031841477

The proceedings contain 32 papers. The special focus in this conference is on Intelligent Technology for Future Transportation. The topics include: Modeling Autonomous Delivery Robots Under Framework of Automated Driving System Using Dynamic Transport Assignment: A State-of-the-Art;virtual Variable Baseline Stereo Vision Algorithm;A Conceptual Framework to Operational Design Domain (ODD)-Based Scenario Generation for Technical Evaluation Obstacle detection in Automated Shuttle Bus;precise Driver’s Drowsiness detection Using a Combination of Proven Methods with a Single Layer Neural Network;fusionSis: Analysis and Evaluation Framework for fusion Safety State of Connected and Automated Vehicles Under Cyber Attacks;deep Learning-Based Monocular Depth estimation Method for Forklift Collision Avoidance;enhancing Multi-user Experience: Optimizing Explanation Timing Through Game Theory;user-Intimate and Trustworthy Ontology-Based Requirement Engineering Methodology: A Case Study on Connected and Autonomous Electrical Vehicles;AI and Leadership in Startup Innovation and Disruption: The Case of Mobility as a Service (MaaS);research on Traffic Analysis Zones Division Model for New Roads Based on Complex Network Theory;forecasting Vehicle Mobility on Various Segments of the Urban Transport Network in the Fuzzy Paradigm;The Emerging ICT for Exposure Reduction in Urban Mobility: Transport Models for Risk Cycle;concept of a Virtual Test Field for Inland Waterway Transport;critical Characterization of Three-Phase Traffic Flow in Severe Condition;design and Implementation of Traffic Congestion Relief Strategies Based on Multi-objective Optimization Algorithms;wide-Area Ship Movement Prediction Using Random Forests;characteristics of Heterogeneous Traffic Flow Involving Different Intelligent Level Autonomous Vehicles;the Research on Customer Demand of Asia-Europe Liner Shipping Companies Based on Kano Model.

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Robust Minimax MMSE for Sparse Signal Recovery Against System Perturbations

Robust Minimax MMSE for Sparse Signal Recovery Against Syste...

引用

international conference on estimation detection and information fusion icedif

作者： Liu, Hongqing Li, Yong Zhou, Yi Huang, Jianzhong Chongqing Univ Posts & Telecommun Chongqing Key Lab Mobile Communicat Technol Chongqing Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Peoples R China LSI Corp San Jose CA USA

ISBN: (纸本)9781479964185

In this work, we develop a minimum mean square error (MMSE) estimator for the underdetermined systems when the signal of interest is sparse. To address the uncertainty issue introduced in the measurement system, robust approaches are developed based on stochastic and worst case optimization techniques under the minimax framework. To solve the optimization problem, different constraints on the unknown signal of interest are considered to transform the minimax optimization into semidefinite programming problem (SDP), which can be efficiently solved. Numerical studies are provided to demonstrate utilizing sparsity and robust approaches indeed improve MMSE estimator when the sparsity of the signal of interest is utilized and the system considered is underdetermined.

关键词： MMSE estimation sparsity perturbations robustness

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Latent Training for Convolutional Neural Networks

Latent Training for Convolutional Neural Networks

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international conference on estimation detection and information fusion icedif

作者： Huang, Zi Liu, Qi Chen, Zhiyuan Zhao, Yuming Shanghai Jiao Tong Univ Sch Electron Informat & Elect Engn Key Lab Syst Control & Informat Proc Minist Educ China Shanghai 200240 Peoples R China

ISBN: (纸本)9781479964185

Pedestrian detection and recognition has become the basic research in various social fields. Convolutional neural networks have excellent learning ability and can recognize various patterns with robustness to some extent distortions and transformations. Yet, they need much more intermediate hidden units and cannot learning from unlabeled samples. In this paper, we purpose a latent training model based on the convolutional neural network. The purposed model adopts part detectors to reduce the scale of the intermediate layer. It also follows a latent training method to determine the labels of unlabeled negative parts. Last, a two-stage learning scheme is purposed to overlay the size of the network step by step. Experimental results on the public static pedestrian detection dataset, INRIA Person Dataset [1], show that our model achieves 98% of the detection accuracy and 95% of the average precision.

关键词： pedestrian detection latent training part detection convolutional neural networks

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Impact Analysis Between Observable Degrees and estimation Accuracy of Kalman Filtering

Impact Analysis Between Observable Degrees and Estimation Ac...

引用

international conference on estimation detection and information fusion icedif

作者： Ma, Jinyan Ge, Quanbo Shao, Teng Hangzhou Dianzi Univ Sch Automat Inst Syst Sci & Control Engn Hangzhou 310018 Zhejiang Peoples R China

ISBN: (纸本)9781479964185

It is well known that estimation performance of the Kalman filtering (KF) depends closely on systemic observability. Moreover, observable degree is usually used to measure the ability of observability on systemic state variables in control and estimation systems. Thereby, there should be a corresponding relation between the estimation performance of the KF and the observable degree. Unfortunately, value of the observable degree can tend to be infinite for most current computational ways and there must be a performance upper bound for the KF estimate. There is a clear impact between the observable degree and the filtering accuracy. Two common approaches to compute observable degree of estimation systems are briefly introduced in this paper, i.e., eigenvalues and eigenvectors analysis method for mean squared error (MSE) and singular value decomposition (SVD) method of observability matrix. Furthermore, the corresponding impact relation between the filtering performance and observable degree is expressly discussed by considering influences from system parameters to the observable degree and the estimation accuracy, respectively. Finally, two simulation examples are given to verify the analysis results obtained in this paper.

关键词： Kalman filter observable degree estimation accuracy SVD systemic parameters

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