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Off-grid DOA estimation via a deep learning framework

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Science China(information Sciences) 2023年第12期66卷 222-237页

作者： Yan HUANG Yanjun ZHANG Jun TAO Cai WEN Guisheng LIAO Wei HONG State Key lab of Millimeter Waves Southeast University Key laboratory of Underwater Acoustic Signal processing of Ministry of Education Southeast University School of information Science and Technology Northwest University National Key lab of Radar Signal processing Xidian University

Direction-of-arrival(DOA) estimation problem is one of the most important tasks for array signal processing. Conventional methods are limited by either the computational complexity or the resolution. In this paper, a novel deep learning(DL) framework for super-resolution DOA estimation is developed, where the grid mismatch problem is fully considered into the DL DOA estimation and the offset between the real DOA and the discrete sampling grid is thereby trained as a part of the network output. Specifically, we first model the DOA estimation problem as a multi-classification task and obtain the on-grid estimation result with a resolution of 1° as the first output. Then we designed another regression module to estimate the offset between target angles and the corresponding grids. By combining both on-grid and offset estimation results, the accurate off-grid estimation is achieved. In addition, the method of fusing low-level features and high-level features by skip connection is adopted between the on-grid estimation module and the offset estimation module. The high-level features in the first part of the network are combined with the original input to improve the training speed and the estimation accuracy. Herein, our training dataset contains a large number of off-grid signal data, which is close to the practical application. Also, the utilization of the off-grid part information improves DOA estimation performance compared to existing DL-based methods,achieving more accurate DOA estimations. Numerous simulation results have demonstrated the superiority of the proposed method in DOA estimation precision, and a good performance advantage maintains even when using a limited number of snapshots. Further, we test the proposed method on the real millimeter-wave radar system, and it outperforms the other state-of-the-art methods, especially for two adjacent targets in the same range and Doppler bin.

关键词： direction-of-arrival (DOA) estimation deep learning (DL) off-grid array signal processing

Fractionally Nyquist Sample Spaced ARMA Blind Equalizer for Direct Signal Recovery in Passive Bistatic Radar

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Chinese Journal of Electronics 2017年第3期26卷 658-666页

作者： WANG Feng SHEN Wei JIANG Defu WEI Shuang Array and information processing laboratory College of Computer and Information Hohai University Nanjing Research Institute of Electronics Technology

This paper proposes a modified signal processing structure based on the fractionally Nyquist sample spaced structure for passive bistatic *** recover the direct signal from the multipath clutter,an equalizer of the Auto regressive moving average(ARMA)type is proposed based on the fractionally Nyquist sample spaced constant modulus *** with the conventional Nyquist sample spaced equalizer,the equalizer of the fractionally Nyquist sample spaced ARMA structure is more effective in dealing with deep fading multipath channels with zeros near the unit *** simulations and real data tests indicate that the proposed approach outperforms the conventional processing structure in terms of both clutter residual and mean square error.

关键词： Passive bistatic radar Constant modulus algorithm Direct signal recovery Multipath clutter cancellation Fractionally spaced equalizer

Analysis of an improved acquisition method for high-dynamic BOC signal

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Journal of Systems Engineering and Electronics 2016年第6期27卷 1158-1167页

作者： Yi Pan Tianqi Zhang Gang Zhang Zhongtao Luo Chongqing Key laboratory of Signal and information processing Chongqing University of Posts and Telecommunications

To reach a compromise between short acquisition time and excellent detection probability for the high-dynamic binary offset carrier (BOC) signal, an overall algorithm based on discrete polynomial-phase transform (DPT) and partial matching filter (PMF)-fast Fourier transform (FFT) algorithm is given. The high order items of received signals are removed by the method of DPT, and the PMF-FFT algorithm is redesigned for the BOC signal. The simulation experiments and theoretical analyses verify that the improved algorithm has attained improvement in acquisition performance. © 2016 Beijing Institute of Aerospace information.

关键词： Bins Mergers and acquisitions Titration

A Regularized Super Resolution Algorithm for Generalized Gaussian Noise

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Journal of Donghua University(English Edition) 2010年第1期27卷 25-35页

作者：陈文方向忠刘立峰蒋伟丁大为乔艳涛 Institute of Image Communication and information processing Shanghai Jiaotong University Computer and information Department Shanghai University of Electric Power Key lab oratory of Intelligent Computing & Signal processing Ministry of EducationAnhui University

In this paper,an iterative regularized super resolution (SR) algorithm considering non-Gaussian noise is *** on the assumption of a generalized Gaussian distribution for the contaminating noise,an lp norm is adopted to measure the data fidelity term in the cost *** the meantime,a regularization functional defined in terms of the desired high resolution (HR) image is employed,which allows for the simultaneous determination of its value and the partly reconstructed image at each iteration *** convergence is thoroughly *** results show the effectiveness of the proposed algorithm as well as its superiority to conventional SR methods.

关键词： super resolution generalized p-Gaussian distribution regularization parameter

Event-triggered MPC Design for Distributed Systems With Network Communications

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 240-250页

作者： Xiaoxiao Mi Shaoyuan Li Key laboratory of System Control and information processing Shanghai Jiao Tong University

This paper deals with the communication problem in the distributed system, considering the limited battery power in the wireless network and redundant transmission among nodes. We design an event-triggered model predictive control(ET-MPC) strategy to reduce the unnecessary communication while promising the system performance. On one hand, for a linear discrete time-invariant system, a triggering condition is derived based on the Lyapunov stability. Here, in order to further reduce the communication rate, we enforce a triggering condition only when the Lyapunov function will exceed its value at the last triggered time, but an average decrease is guaranteed. On the other hand, the feasibility is ensured by minimizing and optimizing the terminal constrained set between the maximal control invariant set and the target terminal set. Finally, we provide a simulation to verify the theoretical results. It's shown that the proposed strategy achieves a good trade-off between the closed-loop system performance and communication rate.

关键词： Event-triggered control Lyapunov stability model predictive control(MPC) terminal invariant set

Spatiotemporal organization of simple-cell receptive fields in area 18 of cat's cortex

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Science China(Life Sciences) 1998年第1期41卷 1-8页

作者：雷静江李朝义 1. Shanghai Institute of Physiology Chinese Academy of Sciences 200031 Shanghai China 2. laboratory of visual information processing Institute of Biophysics Chinese Academy of Sciences 100101 Beijing China

Spatiotemporal structures of receptive fields (RF) have been studied for simple cells in area 18 of cat by measuring the temporal transfer function (TTF) over different locations (subregions) within the RF. The temporal characteristics of different subregions differed from each other in the absolute phase shift (APS) to visual stimuli. Two types of relationships can be seen: (i)The APS varied continuously from one subregion to the next; (ii) A 180° phase jump was seen as the stimulus position changed somewhere within the receptive field. Spatiotemporal receptive field profiles have been determined by applying reverse Fourier analysis to responses in the frequency domain. For the continuous type, spatial and temporal characteristics cannot be dissociated (space time inseparable) and the spatiotemporal structure is oriented. On the contrary, the spatial and temporal characteristics for the jumping type can be dissociated (space time separable) and the structure is not oriented in the space time plane. Based on the APSs measured at different subregions, the optimal direction of motion and optimal spatial frequency of neurons can be predicted.

关键词： visual cortex simple cell receptive field spatiotemporal characteristics response phase direction selectivity.

A parallel ant colonies approach to de novo prediction of protein backbone in CASP8/9

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Science China(information Sciences) 2013年第10期56卷 226-238页

作者： LV Qiang WU HongJie WU JinZhen HUANG Xu LUO XiaoHu QIAN PeiDe School of Computer Science and Technology Soochow University Jiangsu Provincial Key lab for information processing Technologies School of Electronic and information Engineering Suzhou University of Science and Technology

Predicting the three-dimensional structure of proteins from amino acid sequences with only a few remote homologs,or de novo prediction,remains a major challenge in computational *** modeling of the protein backbone represents the initial phase of a protein structure prediction *** a parallel ant colony optimization based on sharing one pheromone matrix,this report proposes a parallel approach to predict the structure of a protein *** parallel approach combines various sources of energy functions and generates protein backbones with the lowest energies jointly determined by the various energy *** free modeling targets in CASP8/9 are used to evaluate the performance of the *** 13 targets in CASP8,two out of the predicted model1s selected by our approach are the best of the published CASP8 results,and seven out of the model1s are ranked in the top *** 29 targets in CASP9,20 out of the best models from our predictions are ranked in the top 10,and 11 out of the model1s are ranked in the top 10.

关键词： protein backbone prediction parallel ant colony optimization

GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling

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Science China(information Sciences) 2025年第1期 145-158页

作者： Wei JU Yiyang GU Zhengyang MAO Ziyue QIAO Yifang QIN Xiao LUO Hui XIONG Ming ZHANG School of Computer Science National Key Laboratory for Multimedia Information ProcessingPeking University Artificial Intelligence Thrust The Hong Kong University of Science and Technology Department of Computer Science University of California

Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown promising performance for representation learning on graphs, which train models by maximizing agreement between original graphs and their augmented views(i.e., positive views). Unfortunately, these methods usually involve pre-defined augmentation strategies based on the knowledge of human experts. Moreover, these strategies may fail to generate challenging positive views to provide sufficient supervision signals. In this paper, we present a novel approach named graph pooling contrast(GPS) to address these *** by the fact that graph pooling can adaptively coarsen the graph with the removal of redundancy, we rethink graph pooling and leverage it to automatically generate multi-scale positive views with varying emphasis on providing challenging positives and preserving semantics, i.e., strongly-augmented view and weakly-augmented view. Then, we incorporate both views into a joint contrastive learning framework with similarity learning and consistency learning, where our pooling module is adversarially trained with respect to the encoder for adversarial robustness. Experiments on twelve datasets on both graph classification and transfer learning tasks verify the superiority of the proposed method over its counterparts.

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Robust and fast iterative sparse recovery method for space-time adaptive processing

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Science China(information Sciences) 2016年第6期59卷 199-211页

作者： Xiaopeng YANG Yuze SUN Tao ZENG Teng LONG Beijing Key laboratory of Embedded Real-time information processing Technology School of Information and Electronics Beijing Institute of Technology

Conventional space-time adaptive processing(STAP) requires large numbers of independent and identically distributed(i.i.d) training samples to ensure the performance of clutter suppression, which is hard to be achieved in practical complex nonhomogeneous environment. In order to improve the performance of clutter suppression with small training sample support, a robust and fast iterative sparse recovery method for STAP is proposed in this paper. In the proposed method, the sparse recovery of clutter spatial-temporal spectrum and the calibration of space-time overcomplete dictionary are achieved iteratively. Firstly, the robust solution of sparse recovery is derived by regularized processing, which can be calculated recursively based on the block Hermitian matrix property, afterwards the mismatch of space-time overcomplete dictionary is calibrated by minimizing the cost function. The proposed method can not only alleviate the effect of noise and dictionary mismatch, but also reduce the computational cost caused by direct matrix inversion. Finally, the proposed method is verified based on the simulated and the actual airborne phased array radar data, which shows that the proposed method is suitable for practical complex nonhomogeneous environment and provides better performance compared with conventional STAP methods.

关键词： space-time adaptive processing(STAP) sparse recovery robust iteration computational complexity