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A Nonorthogonal Uplink/Downlink IoT Solution for Next-Generation ISAC Systems

IEEE INTERNET OF THINGS JOURNAL 2024年第5期11卷 8224-8239页

作者： Liu, Meng Yang, Minglei Wei, Fa Li, Huifang Zhang, Zhaoming Nallanathan, Arumugam Hanzo, Lajos Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England

An integrated sensing and communication (ISAC) system is investigated, where the base station (BS) provides both uplink and downlink Internet of Things (IoT) services as well as target sensing services. Furthermore, nonorthogonal transmission (NO-T) is introduced for improving the spectrum efficiency. The deleterious effects of hardware impairments, channel estimation errors, and imperfect successive interference cancellation are taken into account. Both the exact and asymptotic outage probabilities ( OPs) of the IoT devices as well as the Probability of successful Detection (PoD) are derived for characterizing the communication and sensing (C&S) performances. As a further development, in the presence of the sensing requirements, a communication-centric power allocation (PA) problem is formulated for maximizing the sum rate of the IoT devices. Given the nonconvexity of the problem, an alternating optimization algorithm is developed for finding a near-optimal PA. The simulation results confirm the accuracy of the analysis and demonstrate that: 1) the above nonideal factors degrade the C&S performances;2) the NO-T ISAC system considered outperforms pure ISAC in terms of both its OP and PoD;and 3) compared to other baseline PA schemes, the proposed algorithm maximizes the sum rates while meeting the sensing requirements.

关键词： Internet of Things Sensors Performance evaluation Hardware signal to noise ratio Resource management Optimization Integrated sensing and communication (ISAC) nonorthogonal transmission (NO-T) performance analysis power allocation (PA)

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Properties of focused Laguerre–Gaussian beam propagating in anisotropic ocean turbulence

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Chinese Physics B 2024年第2期33卷 386-393页

作者：王新光马洋斌袁邱杰陈伟王乐赵生妹 College of Science Nanjing University of Posts and TelecommunicationsNanjing 210003China Institute of Signal Processing and Transmission Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Lab of Broadband Wireless Communication and Sensor Network Technology(Ministry of Education) Nanjing University of Posts and TelecommunicationsNanjing 210003China National Laboratory of Solid State Microstructures Nanjing UniversityNanjing 210093China

We analyze the properties of a focused Laguerre–Gaussian(LG)beam propagating through anisotropic ocean turbulence based on the Huygens–Fresnel *** the Rytov approximation theory,we derive the analytical formula of the channel capacity of the focused LG beam in the anisotropic ocean turbulence,and analyze the relationship between the capacity and the light source parameters as well as the turbulent ocean *** is found that the focusing mirror can greatly enhance the channel capacity of the system at the geometric focal plane in oceanic *** results also demonstrate that the communication link can obtain high channel capacity by adopting longer beam wavelength,greater initial beam waist radius,and larger number of transmission ***,the capacity of the system increases with the decrease of the mean squared temperature dissipation rate,temperature-salinity contribution ratio and turbulence outer scale factor,and with the increase of the kinetic energy dissipation rate per unit mass of fluid,turbulence inner scale factor and anisotropy *** to a Hankel–Bessel beam with diffraction-free characteristics and unfocused LG beam,the focused LG beam shows superior anti-turbulence interference properties,which provide a theoretical reference for research and development of underwater optical communication links using focused LG beams.

关键词： vortex beam orbital angular momentum focusing mirror anisotropic turbulence

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radar Emitter Identification with Multi-View Adaptive Fusion Network (MAFN)

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REMOTE SENSING 2023年第7期15卷 1762-1762页

作者： Yang, Shuyuan Peng, Tongqing Liu, Huiling Yang, Chen Feng, Zhixi Wang, Min Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China State Adm Sci Technol & Ind Natl Def Intelligent Decis & Cognit Innovat Ctr Beijing 100048 Peoples R China Xidian Univ Key Lab Radar Signal Proc Xian 710071 Peoples R China

radar emitter identification (REI) aims to extract the fingerprint of an emitter and determine the individual to which it belongs. Although many methods have used deep neural networks (DNNs) for an end-to-end REI, most of them only focus on a single view of signals, such as spectrogram, bi-spectrum, signal waveforms, and so on. When the electromagnetic environment varies, the performance of DNN will be significantly degraded. In this paper, a multi-view adaptive fusion network (MAFN) is proposed by simultaneously exploring the signal waveform and ambiguity function (AF). First, the original waveform and ambiguity function of the radar signals are used separately for feature extraction. Then, a multi-scale feature-level fusion module is constructed for the fusion of multi-view features from waveforms and AF, via the Atrous Spatial Pyramid Pooling (ASPP) structure. Next, the class probability is modeled as Dirichlet distribution to perform adaptive decision-level fusion via evidence theory. Extensive experiments are conducted on two datasets, and the results show that the proposed MAFN can achieve accurate classification of radar emitters and is more robust than its counterparts.

关键词： radar emitter identification multi-view features adaptive fusion evidence theory

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HICEScore: A Hierarchical Metric for Image Captioning Evaluation

arXiv

引用

arXiv 2024年

作者： Zeng, Zequn Wen, Tiansheng Sun, Jiaoqiao Su, Yudi Zhang, Hao Xie, Yan Wang, Zhengjue Chen, Bo National Key Laboratory of Radar Signal Processing Xidian University Xi’an China State Key Laboratory of Integrated Service Networks Xidian University Xi’an China

Image captioning evaluation metrics can be divided into two categories, reference-based metrics and reference-free metrics. However, reference-based approaches may struggle to evaluate descriptive captions with abundant visual details produced by advanced multimodal large language models, due to their heavy reliance on limited human-annotated references. In contrast, previous reference-free metrics have been proven effective via CLIP cross-modality similarity. Nonetheless, CLIP-based metrics, constrained by their solution of global image-text compatibility, often have a deficiency in detecting local textual hallucinations and are insensitive to small visual objects. Besides, their single-scale designs are unable to provide an interpretable evaluation process such as pinpointing the position of caption mistakes and identifying visual regions that have not been described. To move forward, we propose a novel reference-free metric for image captioning evaluation, dubbed Hierarchical Image Captioning Evaluation Score (HICE-S). By detecting local visual regions and textual phrases, HICE-S builds an interpretable hierarchical scoring mechanism, breaking through the barriers of the single-scale structure of existing reference-free metrics. Comprehensive experiments indicate that our proposed metric achieves the SOTA performance on several benchmarks, outperforming existing reference-free metrics like CLIP-S and PAC-S, and reference-based © 2024, CC BY-NC-SA.

关键词： Benchmarking

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MeaCap: Memory-Augmented Zero-shot Image Captioning

MeaCap: Memory-Augmented Zero-shot Image Captioning

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zequn Zeng Yan Xie Hao Zhang Chiyu Chen Bo Chen Zhengjue Wang National Key Laboratory of Radar Signal Processing Xidian University Xi'an China State Key Laboratory of Integrated Service Networks Xidian University Xi'an China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Zero-shot image captioning (IC) without well-paired image-text data can be divided into two categories, training-free and text-only-training. Generally, these two types of methods realize zero-shot IC by integrating pre-trained vision-language models like CLIP for image-text similarity evaluation and a pre-trained language model (LM) for caption generation. The main difference be-tween them is whether using a textual corpus to train the LM. Though achieving attractive performance w.r.t. some metrics, existing methods often exhibit some com-mon drawbacks. Training-free methods tend to produce hallucinations, while text-only-training often lose gener-alization capability. To move forward, in this paper, we propose a novel Memory-Augmented zero-shot image Captioning framework (MeaCap). Specifically, equipped with a textual memory, we introduce a retrieve-then-filter module to get key concepts that are highly related to the image. By deploying our proposed memory-augmented visual-related fusion score in a keywords-to-sentence LM, MeaCap can generate concept-centered captions that keep high consistency with the image with fewer hallucinations and more world-knowledge. The framework of Mea-Cap achieves the state-of-the-art performance on a se-ries of zero-shot IC settings. Our code is available at https://***/joeyzOz/MeaCap.

关键词： Measurement Computer vision Codes Accuracy Pattern recognition Integrated circuit modeling

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Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models

arXiv

引用

arXiv 2025年

作者： Xie, Yan Zeng, Zequn Zhang, Hao Ding, Yucheng Wang, Yi Wang, Zhengjue Chen, Bo Liu, Hongwei National Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China

Concept Bottleneck Models (CBMs) try to make the decision-making process transparent by exploring an intermediate concept space between the input image and the output prediction. Existing CBMs just learn coarse-grained relations between the whole image and the concepts, less considering local image information, leading to two main drawbacks: i) they often produce spurious visual-concept relations, hence decreasing model reliability;and ii) though CBMs could explain the importance of every concept to the final prediction, it is still challenging to tell which visual region produces the prediction. To solve these problems, this paper proposes a Disentangled Optimal Transport CBM (DOT-CBM) framework to explore fine-grained visual-concept relations between local image patches and concepts. Specifically, we model the concept prediction process as a transportation problem between the patches and concepts, thereby achieving explicit fine-grained feature alignment. We also incorporate orthogonal projection losses within the modality to enhance local feature disentanglement. To further address the shortcut issues caused by statistical biases in the data, we utilize the visual saliency map and concept label statistics as transportation priors. Thus, DOT-CBM can visualize inversion heatmaps, provide more reliable concept predictions, and produce more accurate class predictions. Comprehensive experiments demonstrate that our proposed DOT-CBM achieves SOTA performance on several tasks, including image classification, local part detection and out-of-distribution generalization. © 2025, CC BY.

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Sub-Nyquist Tensor Beamformer: A Coprimality Constrained Design

引用

IEEE TRANSACTIONS ON signal processing 2023年 71卷 4163-4177页

作者： Zheng, Hang Zhou, Chengwei Shi, Zhiguo Liao, Guisheng Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Key Lab Collaborat Sensing & Autonomous Unmanned S Hangzhou 310015 Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Zhejiang Univ Int Joint Innovat Ctr Haining 314400 Peoples R China Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

Adaptive beamforming using sparse arrays can alleviate system burden with a sub-Nyquist sampling rate while achieving high resolution. To process multi-dimensional signals without losing structural information, tensor models can be incorporated in the beamformer design. Unfortunately, existing tensor beamformers are only suitable for uniform arrays and cannot handle ambiguous sidelobes caused by sparse sensor deployment. In this article, we propose a sub-Nyquist tensor beamformer based on a coprimality constraint. Specifically, the signals received by the sparse subarrays of a coprime planar array are modeled as two sub-Nyquist tensors. To enhance the desired component of the sub-Nyquist tensor signals, we formulate a pair of tensor beamformer weights and investigate the principle of tensorial signal filtering. A coprimality-based combined distortionless response constraint is then imposed to jointly optimize the tensor beamformer weights, which eliminates spatial aliasing. Moreover, to solve the joint coprime tensor beamformer weights optimization problem with non-convex tensor-based objective and constraint, we decompose it into interconnected bidirectional sub-beamformer optimization problems, which are further relaxed to ensure the convexity. The relaxed problems are solved by an alternating minimization approach with global convergence. Simulation results demonstrate the superiority of the proposed sub-Nyquist tensor beamformer over conventional beamformers in terms of mainlobe enhancement and sidelobe attenuation.

关键词： Tensors Array signal processing Transmission line matrix methods Planar arrays Optimization Sensor arrays Interference Bidirectional sub-beamformer combined distortionless response constraint coprimality sub-Nyquist tensor beamformer

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Terrain effect analysis and compensation method for polarimetric synthetic aperture radar interferometry height parameter inversion

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IET radar SONAR AND NAVIGATION 2022年第12期16卷 1924-1935页

作者： Suo Zhiyong Wang Tingting Xue Chao Zhang Tao Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Sichuan Aerosp Elect Equipment Res Inst Chengdu Peoples R China State Key Lab Space Ground Integrated Informat Te Beijing Peoples R China

In this paper, the terrain effect on parameter inversion performance of polarimetric synthetic aperture radar (PolSAR) and polarimetric SAR interferometry (PolInSAR) are analysed first. Based on the analysis of the terrain effect on single pixel processing, the terrain effect between adjacent pixels is addressed for both PolSAR and PolInSAR image pairs. Through the analysis, the terrain effect should be considered not only for the single pixel data calibration but also for the adjacent pixels of PolSAR and PolInSAR parameter inversion processing. Therefore, combining with the geometric model correction of sloped-random volume over ground, the terrain-compensated procedure is proposed for PolInSAR parameter inversion. The parameter inversion performance is improved effectively with terrain effect consideration. The effectiveness of the proposed method is investigated by the PolSARPro simulated dataset and the real Phased Array L-Band Synthetic aperture radar (PALSAR) dataset.

关键词： parameter inversion polarimetric interferometric SAR (PolInSAR) sloped-random volume over ground (S-RVoG) synthetic aperture radar (SAR) terrain compensation

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signal Source Positioning Based on Angle-Only Measurements in Passive Sensor Networks

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SENSORS 2022年第4期22卷 1554页

作者： Chen, Yidi Wang, Linhai Zhou, Shenghua Chen, Renwen Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Mech Struct 29 Yudao St Nanjing 210016 Peoples R China Xidian Univ Natl Lab Radar Signal Proc 2 Taibai Rd Xian 710071 Peoples R China

Some passive sensors can measure only directions of arrival of signals, but the real positions of signal sources are often desirable, which can be estimated by combining distributed passive sensors as a network. However, passive observations should be correctly associated first. This paper studies the multi-target data association and signal localization problem in distributed passive sensor networks. With angle-only measurements from distributed passive sensors, multiple lines in a 3-dimensional (3D) scenario can be built and then those that will intersect in a small volume in 3D are classified into the same source. The center of the small volume is taken as an estimate of the signal source position, whose statistical distributions are formulated. If the minimum distance is less than an association threshold, then two lines are considered to be from the same signal source. In numerical results, the impacts of angle measurement accuracy and platform self-positioning accuracy are analyzed, indicating that this method can achieve a prescribed data association rate and a high positioning performance with a low computation cost.

关键词： passive sensor network signal localization data association angle-only measurements accuracy analysis

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An Unambiguous Angle and Range Estimation Method using MR-FDA-MIMO radar

An Unambiguous Angle and Range Estimation Method using MR-FD...

引用

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

作者： Zhengxi Wang Shengqi Zhu Ximin Li Lan Lan Congfeng Liu National Key Lab of Radar Signal Processing Xidian University Xi’an China Key Lab of Electronic Information Countermeasure and Simulation Technology Ministry of Education Xidian University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Frequency diversity array (FDA) multiple-input multiple-output (MIMO) radar systems have drawn widespread focus due to their capacity for providing angular and range information simultaneously. Unfortunately, the estimation accuracy is constrained by the amount of physical sensors. This paper proposes a method for unambiguous angle and range estimation using a combination of the minimum redundancy array (MRA) and FDA-MIMO radar. The MRA is applied to extend array degrees of freedom by differential processing. Following the implementation of range compensation, a two-dimensional spatially smoothed algorithm is employed to address the rank deficit issue in the equivalent array. Finally, the target’s angle, ambiguous zone and range offset are identified through a MUSIC algorithm. The efficacy for the designed methodology is corroborated by the simulated experimental outcomes.

关键词： Smoothing methods Array signal processing MIMO radar Estimation Termination of employment radar Frequency diversity Multiple signal classification Sensors Arrays

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