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Improved Capon Estimator for High-Resolution DOA Estimation and Its Statistical Analysis

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1716-1729页

作者： Weiliang Zuo Jingmin Xin Changnong Liu nanning zheng Akira Sano IEEE the National Key Laboratory of Human-Machine Hybrid Augmented Intelligence the National Engineering Research Center for Visual Information and Applicationsand the Institute of Artificial Intelligence and RoboticsXi’an Jiaotong UniversityXi’an 710049China the Data Center China Construction BankBeijing 100033China the Department of System Design Engineering Keio UniversityYokohama 223-8522Japan

Despite some efforts and attempts have been made to improve the direction-of-arrival(DOA)estimation performance of the standard Capon beamformer(SCB)in array processing,rigorous statistical performance analyses of these modified Capon estimators are still lacking.This paper studies an improved Capon estimator(ICE)for estimating the DOAs of multiple uncorrelated narrowband signals,where the higherorder inverse(sample)array covariance matrix is used in the Capon-like cost function.By establishing the relationship between this nonparametric estimator and the parametric and classic subspace-based MUSIC(multiple signal classification),it is clarified that as long as the power order of the inverse covariance matrix is increased to reduce the influence of signal subspace components in the ICE,the estimation performance of the ICE becomes equivalent to that of the MUSIC regardless of the signal-to-noise ratio(SNR).Furthermore the statistical performance of the ICE is analyzed,and the large-sample mean-squared-error(MSE)expression of the estimated DOA is derived.Finally the effectiveness and the theoretical analysis of the ICE are substantiated through numerical examples,where the Cramer-Rao lower bound(CRB)is used to evaluate the validity of the derived asymptotic MSE expression.

关键词： Capon beamformer direction-of-arrival(DOA)estimation large-sample mean-squared-error(MSE) subspace-based methods uniform linear array

Parallel Learning:a Perspective and a Framework

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IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 389-395页

作者： Li Li Yilun Lin nanning zheng Fei-Yue Wang Department of Automation TNListTsinghua University IEEE State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences Institute of Artificial Intelligence and Robotics(IAIR) Xi'an Jiaotong University Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology

The development of machine learning in complex system is hindered by two problems nowadays.The first problem is the inefficiency of exploration in state and action space,which leads to the data-hungry of some state-of-art data-driven algorithm.The second problem is the lack of a general theory which can be used to analyze and implement a complex learning system.In this paper,we proposed a general methods that can address both two issues.We combine the concepts of descriptive learning,predictive learning,and prescriptive learning into a uniform framework,so as to build a parallel system allowing learning system improved by self-boosting.Formulating a new perspective of data,knowledge and action,we provide a new methodology called parallel learning to design machine learning system for real-world problems.

关键词： Descriptive learning machine learning parallel learning parallel systems predictive learning prescriptive learning

《人工智能本科专业知识体系与课程设置》新旧之序

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智能科学与技术学报 2024年第1期6卷 2-4页

作者：郑南宁中国工程院西安交通大学西安交通大学人工智能与机器人研究所中国自动化学会国际模式识别协会(IAPR)理事会

《人工智能本科专业知识体系与课程设置》(第2版)(如图1所示)之序探讨的主要观点是“知识的变与不变”,其第1版(如图2所示)之序探讨的主要观点是“教育也是一种创造”。一、知识的变与不变时间过得真快,仿佛一转身,五年就已过去。

关键词：专业知识体系人工智能课程设置变与不变本科知识

A Correntropy-based Affine Iterative Closest Point Algorithm for Robust Point Set Registration

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IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 981-991页

作者： Hongchen Chen Xie Zhang Shaoyi Du Zongze Wu nanning zheng the School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510640China the School of Electric Power South China University of TechnologyGuangzhou 510640China the Institute of Artificial Intelligence and Robotics Xi’an Jiaotong UniversityXi’an 710049China the School of Automation Guangdong University of TechnologyGuangzhou 510006China IEEE

The iterative closest point(ICP)algorithm has the advantages of high accuracy and fast speed for point set registration,but it performs poorly when the point set has a large number of noisy outliers.To solve this problem,we propose a new affine registration algorithm based on correntropy which works well in the affine registration of point sets with outliers.Firstly,we substitute the traditional measure of least squares with a maximum correntropy criterion to build a new registration model,which can avoid the influence of outliers.To maximize the objective function,we then propose a robust affine ICP algorithm.At each iteration of this new algorithm,we set up the index mapping of two point sets according to the known transformation,and then compute the closed-form solution of the new transformation according to the known index mapping.Similar to the traditional ICP algorithm,our algorithm converges to a local maximum monotonously for any given initial value.Finally,the robustness and high efficiency of affine ICP algorithm based on correntropy are demonstrated by 2D and 3D point set registration experiments.

关键词： Affine iterative closest point(ICP)algorithm correntropy-based robust point set registration

Autonomous driving: cognitive construction and situation understanding

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Science China(Information Sciences) 2019年第8期62卷 75-101页

作者： Shitao CHEN Zhiqiang JIAN Yuhao HUANG Yu CHEN Zhuoli ZHOU nanning zheng Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University National Engineering Laboratory for Visual Information Processing and Applications

Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency,poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes,the efficiency of event-related mechanism is more significant than single-attribute scenario perception data.Therefore, an effective self-driving method should not be confined to the traditional computing framework of‘perception, planning, decision-making, and control’. It is necessary to explore a basic computing framework that conforms to human driver’s attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence. In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors’ research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It further describes how to use multi-sensor and graph d

关键词： autonomous driving event-driven mechanism cognitive construction situation understanding intuitive reasoning

A Co-Point Mapping-Based Approach to Drivable Area Detection for Self-Driving Cars

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Engineering 2018年第4期4卷 479-490页

作者： Ziyi Liu Siyu Yu nanning zheng Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an 710049 China National Engineering Laboratory for Visual Information Processing and Applications Xi'an fiaotong University Xi'an 710049 China

The randomness and complexity of urban traffic scenes make it a difficult task for self-driving cars to detect drivable areas. Inspired by human driving behaviors, we propose a novel method of drivable area detection for self-driving cars based on fusing pixel information from a monocular camera with spatial information from a light detection and ranging (LIDAR) scanner. Similar to the bijection of collineation, a new concept called co-point mapping, which is a bijection that maps points from the LIDAR scanner to points on the edge of the image segmentation, is introduced in the proposed method. Our method positions candidate drivable areas through self-learning models based on the initial drivable areas that are obtained by fusing obstacle information with superpixels. In addition, a fusion of four features is applied in order to achieve a more robust performance. In particular, a feature called drivable degree (DD) is proposed to characterize the drivable degree of the LIDAR points. After the initial drivable area is characterized by the features obtained through self-learning, a Bayesian framework is utilized to calculate the final probability map of the drivable area. Our approach introduces no common hypothesis and requires no training steps; yet it yields a state-of-art performance when tested on the ROAD-KITTI benchmark. Experimental results demonstrate that the proposed method is a general and efficient approach for detecting drivable area.

关键词：城市交通测量方法计算机技术交通系统

Automatic salient object sequence rebuilding for video segment analysis

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Science China(Information Sciences) 2018年第1期61卷 249-260页

作者： Tie LIU Haibin DUAN Yuanyuan SHANG Zejian YUAN nanning zheng School of Automation Science and Electrical Engineering Beihang University Information Engineering College Capital Normal University The School of Electronic and Information Engineering Xi'an Jiaotong University

Detection of salient object sequences from video data is challenging when the salient object changes between consecutive frames. In this study, we addressed the salient object sequence rebuilding problem with video segment analysis. We reformulated the problem as a binary labeling problem, analyzed the potential salient object sequences in the video using a clustering method, and separated the salient object sequence from the background by applying an energy optimization method. Our proposed approach determines whether temporal consecutive pixels belong to the same salient object sequence. The conditional random field is then learned to effectively integrate the salient features and the sequence consecutive constraints.A dynamic programming algorithm was developed to resolve the energy minimization problem efficiently.Experimental results confirmed the ability of our approach to address the salient object rebuilding problem in automatic visual attention applications and video content analysis.

关键词： salient object video attention sequence segment analysis conditional random model

Fractal image encoding with flexible classification sets

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Chinese Science Bulletin 2014年第14期59卷 1597-1606页

作者： Jianji Wang Xuguang Lan Yuehu Liu nanning zheng Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University

Fractal image compression(FIC)technology is an interesting attempt at structure similarity-based image compression.It has been widely applied in many fields such as image encryption,image retrieval,image sharpening,and pattern recognition.However,overlong encoding time is the main difficulty for the application of FIC.In this paper,a new FIC speedup algorithm is proposed with two steps.Firstly,the simplified statistical variable expressions can speed up encoding twice more than the baseline fractal compression(BFC)without loss of image quality corresponding to BFC.Secondly,based on the fact that the affine self-similarity is equivalent to the absolute value of Pearson’s correlation coefficient,a new block classification strategy with flexible classification sets is proposed to speed up encoding further.The experiment results and theoretical analysis show that the proposed scheme achieves high performance in both image quality preservation and encoding efficiency.

关键词：分形图像编码分类策略 Pearson相关系数设置分形图像压缩图像质量变量表达式 FIC

A new implementation of image-processing engine for 3D visualization and stereo video stream display

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Chinese Science Bulletin 2014年第5期59卷 459-467页

作者： Chenyang Ge Zuoxun Hou Huimin Yao nanning zheng Wenzhe Zhao Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University

This paper presents a new implementation of a high-definition image-processing engine,which mainly targets the 3-dimensional(3D)visualization and stereo video stream display of binocular display equipment.The engine is compatible with the mainstream analog and digital stereo videos in component format and is able to receive stereo composite video broadcast signals using an integrated analog stereo video decoder.The four modules include a spatiotemporal scaling transform engine,a 2D–3D converter,an image animating engine,and a 2D scalar operating in pipeline architecture to implement the video format conversion and the stereo effect enhancement.Furthermore,the data access,hardware structure,and system-level configurations are optimized.Finally,the proposed architecture is realized by 0.18 lm CMOS technology.The application-specific integrated circuit verification results show that the engine can generate a strong feeling of 3D immersion and highdefinition image quality with minimal flicker.The chip has wide compatibility and an uppermost 1080P-processing capacity,which has approximately 3.5 million gates with about 43 mm2 die size.

关键词：图像处理引擎数字立体声三维可视化显示设备视频流模拟立体声视频格式转换 CMOS技术