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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ganggang Dong Zixuan Wang National Key Lab of Radar Signal Processing Xidian University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The discernment of radar jamming signal played an important role for the downstream tasks. Great performance was achieved by the deep learning methods. Yet large amounts of labeled signals were needed. To solve the problem, a new cross-modality contrast learning method was proposed in this paper. The signal-wise hierarchy, and the image-wise hierarchy were developed to extract the features from IQ data (In-phase & quadrature) and TF-image (Time-frequency). The cross-domain features were then combined. The fused features were delivered to the learning phase. It was composed of the pre-training and the fine-tuning. The unlabeled signals were first employed to optimize the similarity loss to make the positive sample mores similar to the signal than the negative samples. The pre-trained model was then fine-tuned to the recognition task. The proposed method was demonstrated to provide impressive performance by multiple comparative studies.

关键词： Time-frequency analysis radar Speech recognition signal processing radar imaging Feature extraction Multitasking Jamming Speech processing Standards

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Sparse Bayesian Network for Fast Micro-Doppler Analysis

Sparse Bayesian Network for Fast Micro-Doppler Analysis

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2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Jiongge Dong, Hang Tian, Long He, Xiongpeng Sun, Huimin Liu, Yuan Pilot School of Software Engineering Xidian University Xi'an China Key Lab. of Radar Signal Processing Xidian University Xi'an China Electronic Engineering Research Institute Xi'an China Nanyang Technological University Singapore

ISBN: (纸本)9798350368741

Micro-Doppler Analysis (MDA) of rigid-body targets is crucial for various practical downstream tasks such as target imaging and recognition. radar echoes from micro-moving targets typically represent non-stationary signals and are often described using the parameterized Time-Varying Auto Regressive (TVAR) model. Sparse Bayesian Learning (SBL) is commonly employed to estimate time-invariant coefficients, thereby achieving high-resolution micro-Doppler time-frequency distributions. Despite its effectiveness, SBL-based optimization methods often face inefficiency due to the computational burden of inverse operation. To address this challenge and enhance the efficiency of MDA based on TVAR model, we propose a Sparse Bayesian Network (SBN) that unfolds a fast Mean Field SBL (MF) using a deep variational autoencoding framework. This method retains the optimization effectiveness of SBL while incorporating the inference efficiency of Deep Neural Networks (DNNs). Our proposed method demonstrates strong generalization capabilities, performing well on both simulated and measured radar echoes. © 2025 IEEE.

关键词： Micro-Doppler Analysis (MDA) Rigid-body Target Sparse Bayesian Network (SBN)

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A coordinate-transform based FFBP algorithm for high-resolution spotlight SAR imaging

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Science China(Information Sciences) 2015年第2期58卷 31-41页

作者： YANG ZeMin XING MengDao ZHANG Lei BAO Zheng National Key Lab of Radar Signal Processing Xidian University

This paper proposes a coordinate-transform(CT) implementation for the fast factorized backprojection(FFBP) algorithm(CT-FFBP) to process high-resolution spotlight synthetic aperture radar(SAR)data. Unlike the FFBP utilizing two-dimensional image-domain interpolation for sub-aperture fusion, CT-FFBP finishes the image-projection using CT with the accommodation of chirp-z transform and circular shifting. Without interpolation, CT-FFBP yields enhanced efficiency over the interpolation based FFBP, besides maintaining high precision simultaneously. Both simulation and real-data experiments verifies the efficiency and precision superiorities of the CT-FFBP.

关键词： spotlight synthetic aperture radar(SAR) fast factorized back-projection(FFBP) coordinate-transform(CT)

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HNet: A Loss Rate Robust ISAR Imaging Framework Based on Hypernetwork

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Li, Xiaoyong Liu, Lei Huang, Jiale Li, Lei Bai, Xueru Zhou, Feng Xidian University Hangzhou Institute of Technology Hangzhou311231 China Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi'an710071 China Xidian University National Key Lab of Radar Signal Processing Xi'an710071 China Xidian University School of Aerospace Science and Technology Xi'an710071 China

Deep learning has significantly advanced the field of sparse inverse synthetic aperture radar (ISAR) imaging. Nonetheless, current methodologies often face constraints due to their reliance on fixed loss rates and types, which can limit practical applicability and result in elevated computational demands both spatially and temporally, as well as diminished generalizability. In tackle to these challenges, this paper introduces a resilient ISAR imaging architecture grounded in hypernetwork technology capable of accommodating diverse loss rates. Initially, we devise a sparse signal reconstruction technique that functions irrespective of the loss rate or matrix dimensions. Subsequently, a methodology for producing arbitrary loss rates is outlined. Building upon this foundation, we present a hypernetwork framework that harnesses hypernetworks to derive defocus characteristics from range-Doppler (RD) imagery across varying loss rates, subsequently transforming these insights into ideal parameters for the sparse signal reconstruction process. By executing the iterative algorithm with these optimized parameters, we achieve loss rate robust ISAR imaging. Comprehensive evaluations conducted on point simulated data, electromagnetic simulated data and measured data confirm the superiority of our proposed approach in terms of reconstruction accuracy and generalization capabilities. This advancement facilitates reliable ISAR imaging across a spectrum of loss rates. 1965-2011 IEEE.

关键词： radar imaging

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Linear feature detection based on ridgelet

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Science China(Technological Sciences) 2003年第2期46卷 141-152页

作者：侯彪刘芳焦李成 Key Lab for Radar Signal Processing Xidian University

Linear feature detection is very important in image processing. The detection efficiency will directly affect the performance of pattern recognition and pattern classification. Based on the idea of ridgelet, this paper presents a new discrete localized ridgelet transform and a new method for detecting linear feature in anisotropic images. Experimental results prove the efficiency of the proposed method.

关键词： linear feature detection, ridgelet transform, wavelet transform, Radon transform.

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Sparse Bayesian Network for Fast Micro-Doppler Analysis

Sparse Bayesian Network for Fast Micro-Doppler Analysis

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jiongge Zhang Hang Dong Long Tian Xiongpeng He Huimin Sun Yuan Liu Pilot School of Software Engineering Xidian University Key Lab. of Radar Signal Processing Xidian University Pilot School of Software Engineering Xidian University Xi’an China Electronic Engineering Research Institute Xi’an China Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Time-frequency analysis Target recognition radar measurements Computational modeling Optimization methods radar radar imaging signal processing Bayes methods Speech processing

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Direction synthesis in DOA estimation for monostatic multiple input multiple output (MIMO) radar based on synthetic impulse and aperture radar (SIAR) and its performance analysis

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Science China(Technological Sciences) 2008年第6期51卷 656-673页

作者： ZHAO GuangHui CHEN BaiXiao ZHU ShouPing National Key Lab for Radar Signal Processing Xidian UniversityXi’an 710071China

A new direction synthetic method for monostatic multiple input multiple output (MIMO) radar is presented based on synthetic impulse and aperture radar (SIAR) system. Concerned with the monostatic MIMO radar which simultaneously emits orthogonal signals with multi-carrier-frequency and possesses sparsely distributed transmitting and receiving arrays with respective location, as well as the situation for the presence of multipath propagation in the low flying target’s echo, the method integrates the aperture of the transmitting arrays with the receiving arrays to form the digital beam-forming (DBF) in azimuth and elevation dimensions. And a study has been made of planar general MUSIC algorithm based on decorrelating the multipath signals of multi-carrier-frequency MIMO radar. Through compensat-ing the phase delay of both the transmitting and the receiving arrays and synthe-sizing the transmitting beam in two dimensions at the receiver, the angular resolu-tion and measurement accuracy are improved and the computational complexity is reduced after transforming the three-dimensional (3D) parameter estimation prob-lem into a two-dimensional (2D) one. Finally, the Cramer-Rao Bounds (CRBs) of DOA estimation for azimuth and elevation is put forward with the exsiting multipath propagation. Results of computer simulation demonstrate the validity of the new method.

关键词： SIAR monostatic MIMO radar multipath propagation super resolution general MUSIC algorithm Cramer-Rao Bounds

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Identification of Nonlinear Systems Using Neurofuzzy Networks

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中国电子杂志（英文版） 2001年第3期10卷 393-396页

作者： LI Ying JIAO Licheng Key Lab for Radar Signal Processing Xidian University Xi'an 710071 China

This paper presents a compound neu-ral network model, I.e., adaptive neurofuzzy network(ANFN), which can be used for identifying the com-plicated nonlinear system. The proposed ANFN has asimple structure and exploits a hybrid algorithm com-bining supervised learning and unsupervised *** addition, ANFN is capable of overcoming the errorof system identification due to the existence of somechanging points and improving the accuracy of identi-fication of the whole system. The effectiveness of themodel and its algorithm are tested on the identifica-tion results of missile attacking area.

关键词： Adaptive neurofuzzy network(ANFN) Fuzzy clustering System identification.

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High-resolution algorithm based on temporal-spatial extrapolation

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Journal of Systems Engineering and Electronics 2010年第1期21卷 9-15页

作者： Xueya Yang Baixiao Chen Feilin Qi National Key Lab for Radar Signal Processing Xidian University Xi' an 710071 E R. China

To enhance the resolution of parameter estimation with limited samples received by a short passive array, an iterative nonparametric algorithm for estimating the frequencies and direction-of-arrivals （DOAs） of signals is proposed. The cost function is constructed using 12-norm Gaussian entropy combined with an additional constraint, 12-norm constraint or linear constraint. By minimizing the cost functions in the temporal and the spatial dimensions using corresponding iteration algorithms respectively, the sparse discrete Fourier transforms （DFTs） of temporal and spatial samples are obtained to represent the extrapolated sequences with much larger sizes than the original samples. Then frequency and angle estimates are obtained by performing the traditional simple methods on the extrapolated sequences. It is shown that the proposed algorithm offers increased resolution and significantly reduced sidelobes compared with the periodogram and beamforming based methods. And it achieves high precision compared with the high-resolution method with lower computational burden. Some numerical simulations and real data processing results are presented to verify the effectiveness of the method.

关键词： temporal-spatial extrapolation frequency estimation direction-of-arrival discrete Fourier transform.

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Global optimum method for motion compensation in ISAR imagery

Global optimum method for motion compensation in ISAR imager...

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radar 97 Conference

作者： Wang, K Luo, L Bao, Z Key Lab for Radar Signal Processing China

ISBN: (纸本)0852966989

A method finding the global optimum value of misalignment is presented to increase the accuracy of range alignment. There exist the drift errors and the jump errors when the classical amplitude correlation method is used to align the envelope of the echoes. Sometimes, these errors are so great that they are as large as the length of several range bins. There is a marked drop in the quality of the image result. After using the Global Optimum Method, the range alignment is more accurate, the phenomenon of the drift and the jump errors in echo envelope is decreased and the quality of the image is improved. From the image result of real measured data, it can be seen that this method is much better than the classical method.

关键词： ISAR motion compensation global optimum amplitude correlation

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