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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Guo, Hanyue Zhou, Rui Put, Wenqiang You, Ming-Yi Yan, JunKun Hangzhou Institute of Technology Xidian University Hangzhou China Shenzhen Research Institute of Big Data The Chinese University of HongKong Shenzhen China National Key Laboratory of Electromagnetic Space Security Jiaxing China National Key Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9798350368369

Integrated Sensing and Communication (ISAC) systems are at the forefront of next-generation wireless technologies, enhancing high-precision target localization. Accurate prediction of localization performance is crucial for the design and optimization of these systems. Traditionally, the Cramer- Rao Lower Bound (CRLB) has been used as a theoretical benchmark for estimating localization errors, but it often does not reflect actual errors encountered in practice. The Monte Carlo simulation method, while accurate, is computationally intensive and less adaptable to varying parameters. To bridge this gap, we introduce LocNet-Mono, a novel approach based on monotonic neural networks, designed specifically for predicting localization errors. This approach maintains a consistent, monotonic relationship between input and output features, addressing the shortcomings of traditional methods. Our numerical experiments validate the high accuracy and efficiency of LocNet-Mono, confirming its potential as a superior tool for performance prediction in ISAC systems. © 2025 IEEE.

关键词： Passive networks

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Cross-Modal Alignment for End-to-End Spoken Language Understanding Based on Momentum Contrastive Learning

Cross-Modal Alignment for End-to-End Spoken Language Underst...

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

作者： Beida Zheng Mijit Ablimit Askar Hamdulla School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China School of Future Technology Xinjiang University Urumqi China

The end-to-end spoken language understanding system extracts the semantic intent directly from an input speech. It effectively avoids problems such as semantic drift in traditional cascade models. However, the lack of semantically labeled speech data makes the model training process diffi-cult. Several recent multi-modal research perspectives have demonstrated that aligning speech and text embeddings based on space distance can improve the model’s performance. In this study, inspired by the work related to contrastive learning, a speech and text aligning method using momentum contrast learning is proposed, and a momentum distillation method is also used in the model to learn from imperfectly matched speech and text data. The proposed method has improved intent detection accuracy by 2.14% and 5.98% on Fluent Speech Command and SmartLights datasets.

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Passive Localization of Wideband Near-Field Sources Using Sparse Bayesian Learning and Envelope Alignment

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SSRN 2023年

作者： Zhang, Xiaoyu Tao, Haihong Xie, Jian Fang, Ziye National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China School of Computer Science and Technology Xidian University Xi’an710071 China

Most studies about passive localization of near-field signals are limited to narrowband sources. In this paper, a novel efficient algorithm is developed for direction of arrival (DOA) and range estimation of wideband near-field sources. Using the specific structure of symmetric linear array, the direction information is extracted and the off-grid Sparse Bayesian learning (SBL) framework is established for DOA estimation. The generalized approximate message passing (GAMP) strategy is applied for fast implementation. Then the envelope aligned approach is proposed for the range estimation in time domain and the linear searching concept is applied to accelerate the computation. Compared with the existing wideband near-field localization methods, the proposed algorithm has better performance of accuracy and resolution probability. Simulation results validate the feasibility and effectiveness of the proposed algorithm. © 2023, The Authors. All rights reserved.

关键词： Message passing

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High-speed maneuvering target detector based on deep residual network

High-speed maneuvering target detector based on deep residua...

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IET International radar Conference (IRC 2023)

作者： Wenqi Jiang Bo Jiu Yu Zhao Hongwei Liu Yu Zhang Chunlei Wang National Key Laboratory of Radar Signal Processing Xidian University Xi'an People's Republic of China Nanjing Research Institute of Electronics Technology Nanjing People's Republic of China

ISBN: (数字)9781837240982

Range migration and Doppler frequency migration lead to severe coherent integration loss and worse target detection performance in high-speed maneuvering target detection. In this paper, a deep residual network detector (RND) is proposed for high-speed maneuvering target detection. The RND is used to replace the Doppler processing and hypothesis testing components in the conventional signal processing procedure. By training, the RND is capable of effectively achieving a nonlinear mapping from range-compressed signals to target detection results, demonstrating that the proposed detector exhibits excellent detection performance. Finally, numerical experiments validate that the detection performance of the RND is better than that of the KT-MF algorithm in the scenario of higher signal-to-noise ratio, and the computational complexity is much lower than that of the KT-MF algorithm, which can demonstrate the RND can achieve a trade-off between detection performance and computational complexity.

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Gridless Sparse Recovery Stap Algorithm with Array Amplitude-Phase Errors for Non-Uniform Linear Array

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SSRN 2023年

作者： Tan, Xiao Yang, Zhiwei Li, Xianghai Mao, Yongfei Jiang, Di National Key Laboratory of Radar Signal Processing Xidian University Shaanxi Xi’an710071 China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing100094 China

In the continuous angle-Doppler profile, the gridless sparse recovery (SR) space-time adaptive processing (STAP) prevents the off-grid issue of conventional SR-STAP. However, the applicability of the technique is limited to uniform linear array (ULA), whose performance deteriorates significantly owing to the array amplitude-phase errors (AAPEs). To this end, this study proposes a novel gridless SR-STAP algorithm applicable to a non-ULA with AAPEs. First, considering the properties of clutter covariance matrix (CCM), the atomic norm minimization (ANM)-STAP model with AAPEs is established for non-ULA. Second, the ANM-STAP is derived as a joint optimization problem for the clutter subspace and AAPEs, which are iteratively updated using the alternating direction method of multipliers. Finally, CCM is reconstructed to design the STAP filter weight vector. Simulation results show that the proposed algorithm is advantageous for non-ULAs and improves the STAP performance in the presence of AAPEs. © 2023, The Authors. All rights reserved.

关键词： Space time adaptive processing

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Subarrayed array thinning using iterative FFT

Subarrayed array thinning using iterative FFT

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IET International radar Conference (IRC 2023)

作者： Bingfan Liu Zhenyu Li Baixiao Chen Bing Gao Huaigen Zhang Nanjing Research Institute of Electronics Technology Nanjing People's Republic of China National Laboratory of Radar Signal Processing Xidian University Xian 710071 People's Republic of China

ISBN: (数字)9781837240982

This paper proposes a fast-Fourier-transform-based iterative technique for the synthesis of a large thinned planar array with a subarray. Unlike array thinning at the element level, for array thinning at the subarray level, the overlapping of subarrays should be examined. In each iteration, the proposed algorithm is applied to the set of all possible subarrays to select some nonoverlapping subarrays with excitation similar to the expected excitation. The selected subarrays constitute the optimal thinned array with the lowest sidelobe level. Numerical experiments involving a thinned planar array with regular and irregular subarrays showed the effectiveness of the proposed method.

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Frequency Control Scheme for Target Tracking under Dynamic Electromagnetic Environment

Frequency Control Scheme for Target Tracking under Dynamic E...

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signal, Information and Data processing (ICSIDP), IEEE International Conference on

作者： Peng Zhang Junkun Yan Jiaxing Wang Wenqiang Pu Xudong Dang Hongwei Liu National Key Laboratory of Radar Signal Processing Xidian University Xi’an China Hangzhou Institute of Technology Xidian University Hangzhou China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

This paper proposes a frequency control (FC) scheme for radar systems that aims to improve target tracking accuracy under dynamic jamming environments. Specifically, the radar must alter the transmit frequency to reduce the impact of jamming. To combat the dynamic jammer, we construct FC as a Markov Decision Process (MDP) within a reinforcement learning framework. By applying a policy iteration technique to the MDP, an optimal strategy can be learned guiding the radar how to choose the transmit frequency at every frame. Since the entire target tracking process is real-time, a trade-off method is to balance the relationship between the model update and FC scheme utilization until online tracking process is over. Simulation results indicate that the proposed method can evidently beat the dynamic jamming and improve the target tracking accuracy.

关键词： Target tracking Accuracy Simulation Markov decision processes radar Reinforcement learning radar tracking Real-time systems Jamming Frequency control

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Deployment Optimization for Search Task in Netted radar System Under Region Constraints

Deployment Optimization for Search Task in Netted Radar Syst...

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International Conference on signal processing Systems, ICSPS

作者： Tong Zhang Junkun Yan Lan Du Heng Xu Hongwei Liu National Laboratory of Radar Signal Processing Xidian University Xian China Shanghai Aerospace Electronic Technology Institute Shanghai China

This paper studies a deployment optimization method of netted radar systems under radar location constraints. The key idea is to improve the search capability by radar deployment optimization. We first adopt the surveillance region as the metric function to gauge targets' searching performance. Then, various constraints are considered and the optimization model is introduced as a nonconvex optimization problem. To solve this problem, we use the simulated annealing (SA) algorithm to acquire the deployment optimization scheme. Finally, simulation results demonstrate that the deployment proposed can enlarge netted radar systems' detection area, which verifies the validity of the radar deployment scheme proposed.

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Performer: A High-Performance Global-Local Model-Augmented with Dual Network Interaction Mechanism

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IEEE Transactions on Cognitive and Developmental Systems 2024年

作者： Tan, Dayu Hao, Rui Hua, Linfeng Xu, Qi Su, Yansen Zheng, Chunhou Zhong, Weimin Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China

In deep learning, Convolutional Neural Networks (CNNs) focus on local information through convolutional kernels, while transformers attend to global information using self-attention mechanisms. The union of these distinct approaches enables a more comprehensive extraction of image features. However, the feature map dimensions of CNN and Transformer differ, leading to dimension mismatch issues when combining these architectures. Additionally, the parameter size of the hybrid model integrating both architectures remains large, making it difficult to train. To further augmenting the interpretation of complex image patterns, we present Performer, a dual-network architecture that seamlessly combines CNNs and transformers, resulting in a novel and efficient representation learning model. In the Performer model, we innovate by devising a unique interaction methodology for CNN and transformer architectures to enhance the image feature extraction capabilities mutually. To counteract issue of dimensionality mismatch, we also introduce a refined transformer block, a advancement over the transformer block of ViT. To validate the effectiveness of Performer, we conduct extensive experiments on both classification and segmentation tasks. Performer achieve an accuracy of 83.37% on the ImageNet-200 dataset. For semantic segmentation, Performer excels on the CamVid and Hippocampus datasets. On CamVid, our model achieves a mean Intersection over Union (mIoU) of 63.27% and Pixel Accuracy of 92.11%, demonstrating superior performance in capturing fine details and handling complex scenes effectively. The code is available at https://***/hlfthh/ Performer. © 2016 IEEE.

关键词： Semantic Segmentation

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Spatial Undersampling SAS Imaging Algorithm Utilizes Dual Carrier Frequency Azimuth Compressed Sensing

Spatial Undersampling SAS Imaging Algorithm Utilizes Dual Ca...

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OCEANS

作者： Kun Ji Peng Wang Yanbo Jiao Pengfei Zhang Shiping Chen Lulu Ren Linzhe Wei Insititute of Acoustics Chinese Academy of Sciences Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing Chinese Academy of Sciences The University of Chinese Academy of Sciences

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

Restricted by factors such as sonar carrier size, carrier velocity, underwater sound velocity and sound propagation characteristics, there are certain limitations in the mapping efficiency of synthetic aperture sonar(SAS). Spatial undersampling occurs in the azimuth when the moving speed of SAS is too high, resulting in grating lobe, pseudo-targets and blurred imaging azimuths. To address this problem, this paper proposes a spatial undersampling SAS imaging algorithm based on dual carrier frequency azimuth compressed sensing, the algorithm introduces the scene sparse a priori information in azimuth, transmits dual carrier frequency signals, and improves the traditional range doppler(RD) algorithm based on the theory of compressed sensing, which achieves a better undersampling imaging. Then, the grating lobe and pseudo target caused by high-speed undersampling is further suppressed by using the property that the position of the grating lobe is related to the carrier frequency. The simulated imaging results and experiments show that when the mapping speed exceeds the theoretical limit speed of SAS, i.e., when there is serious spatial undersampling in azimuth, the proposed algorithm still achieves high-resolution imaging, effectively suppresses the grating lobe and pseudo targets caused by the undersampling, and greatly improves the mapping efficiency.

关键词： Azimuth Oceans Imaging High-resolution imaging Apertures Frequency conversion Sensors

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