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Enhanced Automotive Radar Collaborative Sensing by Exploiting Constructive Interference

Enhanced Automotive Radar Collaborative Sensing by Exploitin...

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sensor array and multichannel signal processing workshop

作者： Lifan Xu Shunqiao Sun A. Lee Swindlehurst Department of Electrical and Computer Engineering University of Alabama Tuscaloosa AL Center for Pervasive Communications and Computing University of California Irvine CA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Automotive radar emerges as a crucial sensor for autonomous vehicle perception. As more cars are equipped radars, radar interference is an unavoidable challenge. Unlike conventional approaches such as interference mitigation and interference avoiding technologies, this paper introduces an innovative collaborative sensing scheme with multiple automotive radars that exploits constructive interference. Through collaborative sensing, our method optimally aligns cross-path interference signals from other radars with another radar's self-echo signals, thereby significantly augmenting its target detection capabilities. This approach alleviates the need for extensive raw data sharing between collaborating radars. Instead, only an optimized weighting matrix needs to be exchanged between the radars. This approach considerably decreases the data bandwidth requirements for the wireless channel, making it a more feasible and practical solution for automotive radar collaboration. Numerical results demonstrate the effectiveness of the constructive interference approach for enhanced object detection capability.

关键词： Wireless communication Wireless sensor networks Radar cross-sections Collaboration Radar detection Radar Interference

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Distributed Sparse Subspace Clustering by K-Means Subspace Fusion

Distributed Sparse Subspace Clustering by K-Means Subspace F...

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sensor array and multichannel signal processing workshop

作者： Liang-Chi Huang Y.-W. Peter Hong Jwo-Yuh Wu Institute of Communications Engineering National Yang Ming Chiao Tung Univ. Hsinchu Taiwan Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Sparse subspace clustering (SSC) achieves evidenced success in many areas but most related works focus on the centralized scenario. In many practical applications such as security surveillance, several distributed agents jointly collect a large dataset, while each one is however just able to observe a subset through which a local inference is to be made. This paper proposes a collaborative distributed SSC scheme targeted for this situation. In the proposed scheme, each agent first conducts a fast local data clustering, and transmits basis matrices of the estimated subspaces to the data center, which then conducts subspace information fusion using a k-means type method; the aggregated subspace information is fed back to local agents to update their data partitions. Computer simulations using real human face data are used to illustrate the effectiveness of the proposed method.

关键词： Data centers Protocols Computer simulation Surveillance signal processing algorithms Collaboration Distributed databases

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Distributed Sparse Covariance Matrix Estimation

Distributed Sparse Covariance Matrix Estimation

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sensor array and multichannel signal processing workshop

作者： Wenfu Xia Ziping Zhao Ying Sun School of Info. Sci. and Tech. ShanghaiTech University Shanghai China School of Elec. Eng. and Comp. Sci. The Pennsylvania State University PA USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Covariance matrix estimation is a crucial problem in many areas related to data analysis. While centralized sparse covariance matrix estimators have received extensive attention, practical considerations such as communication efficiency and privacy constraints often make centralizing data impractical in many real-world scenarios. This necessitates the development of distributed covariance matrix estimation methods. In this paper, we present a novel distributed estimator for a sparse covariance matrix over networks by minimizing the sum of all agents' losses based on $\ell_{1}$ penalized Gaussian likelihood. To solve this constrained non-convex, non-Lipschitz-smooth optimization problem without relying on a central processor, we propose a straightforward network covariance iterative shrinkage-thresholding algorithm (network C-ISTA) with provable convergence. Numerical simulations demonstrate the convergence and impressive estimation performance of the network C-ISTA algorithm, confirming its effectiveness under decentralized settings.

关键词： Maximum likelihood estimation Data privacy Data analysis signal processing algorithms Numerical simulation Iterative algorithms Covariance matrices

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Radar Anti-Jamming Strategy Learning via Domain-Knowledge Enhanced Online Convex Optimization

Radar Anti-Jamming Strategy Learning via Domain-Knowledge En...

引用

sensor array and multichannel signal processing workshop

作者： Liangqi Liu Wenqiang Pu Yingru Li Bo Jiu Zhi-Quan Luo Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China National Key Laboratory of Radar Signal Processing Xidian University Xi'an China

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

The dynamic competition between radar and jammer systems presents a significant challenge for modern Electronic Warfare (EW), as current active learning approaches still lack sample efficiency and fail to exploit jammer's characteristics. In this paper, the competition between a frequency agile radar and a Digital Radio Frequency Memory (DRFM)-based intelligent jammer is considered. We introduce an Online Convex Optimization (OCO) framework designed to illustrate this adversarial interaction. Notably, traditional OCO algorithms exhibit suboptimal sample efficiency due to the limited information obtained per round. To address the limitations, two refined algorithms are proposed, utilizing unbiased gradient estimators that leverage the unique attributes of the jammer system. Sub-linear theoretical results on both static regret and universal regret are provided, marking a significant improvement in OCO performance. Furthermore, simulation results reveal that the proposed algorithms outperform common OCO baselines, suggesting the potential for effective deployment in real-world scenarios.

关键词： Simulation Frequency-domain analysis signal processing algorithms Radar Numerical simulation Digital communication Convex functions

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Enhanced Jamming Suppression in Colocated MIMO Radar with Fluid Antenna array

Enhanced Jamming Suppression in Colocated MIMO Radar with Fl...

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sensor array and multichannel signal processing workshop

作者： Linlong Wu Bhavani Shankar M. R. Wei Liu Björn Ottersten Interdisciplinary Center for Security Reliability and Trust (SnT) University of Luxembourg School of Electronic Engineering and Computer Science Queen Mary University of London

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Beyond traditional solid-state antenna, fluid antennas (FAs) exhibit unparalleled reconfigurability, drawing significant interest for applications in wireless communications and radar. This paper presents a design of FA array (FAA) MIMO radar for improved target detection in the presence of jammers, highlighting the advantages of FAA in sensing scenarios. By utilizing flexible positioning of FAAs, we introduce the antenna position vector (APV) as a design variable, in addition to waveforms, aiming to maximize the signal-to-interference plus noise ratio (SINR) with constraints to maintain waveform unimodularity and avoid FA coupling. The formulated nonconvex problem is tackled by an iterative algorithm based on the block majorization-minimization framework. Each iteration involves solving linearly constrained quadratic programming problems for APV optimization and updating the waveforms via a closed-form solution. Simulation results reveal that the designed APV s of the transmit and receiving FAAs can automatically balance angular resolution and ambiguity, which together with the optimized waveform, significantly enhances the SINR through enhanced jamming suppression. This improvement is attributed to increased flexibility across spatial and frequency dimensions facilitated by the FAAs.

关键词： MIMO radar Interference Object detection Radar antennas Iterative methods Jamming Optimization

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Block Sparsity Based Channel Estimation for IRS-Assisted mmWave MIMO Systems

Block Sparsity Based Channel Estimation for IRS-Assisted mmW...

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sensor array and multichannel signal processing workshop

作者： Fang Guo Zhenhua Zhou Bin Liao Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen Guangdong Province China

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

In this paper, we consider the downlink channel estimation for the intelligent reflecting surface (IRS) assisted MIMO systems in the millimeter wave band. It is challenging to perform the channel estimation for the IRS-assisted wireless communication systems, due to the large amount of passive reflecting elements at the IRS. On account of the inherent sparsity of millimeter-wave channels, the sparse representation expressions of the channel between the base station (BS) and IRS and that between the IRS and user equipment (UE) are obtained separately. Furthermore, the cascaded channel is estimated by solving the BS-IRS and IRS-UE channels in an alternating way. To improve the estimation accuracy and reduce the pilot over-head, the algorithm of the block-structured orthogonal matching pursuit (BS-OMP) is devised to estimate the BS-IRS channel by exploiting its block sparsity. The simulation results show that the proposed method provides the higher estimation accuracy and consumes less pilot overhead by comparing with its counterparts.

关键词： Wireless communication Accuracy Simulation Millimeter wave technology Channel estimation Matching pursuit algorithms Estimation

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A Machine- Learning -based approach to Direction-of-arrival Sectorization using Spherical Microphone array

A Machine- Learning -based approach to Direction-of-arrival ...

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sensor array and multichannel signal processing workshop

作者： Chibuzo Joseph Nnonyelu Meng Jiang Marianthi Adamopoulou Jan Lundgren STC Research Center Mid Sweden University Sundsvall Sweden

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Direction of arrival estimation using the spherical microphone array usually requires a search in the whole 3-dimensional space, hence computationally demanding. This work presents a machine learning approach to sectorizing the 3-dimensional space, as an intermediate step for direction-of-arrival estimation using spherical microphone array. A new feature based on the outer product of spherical harmonic vectors was proposed for the classification. This spherical harmonic matrix nominally offers lower dimensionality compared to the commonly used covariance matrix of received data. The dimension of the input matrix was further reduced using the neighborhood component analysis. The extracted features were then used to train a support vector machine (SVM), 2-layer multilayer perceptron (MLP) and a convolutional neural network (CNN) for classification purposes. The results show that the models were able to classify the spherical sector with up to 90 % accuracy for all models and number of sectors under consideration. Also, the MLP and CNN trained with simulated samples were able to accurately classify samples from real data that were not included in training samples.

关键词： Support vector machines Training Direction-of-arrival estimation Machine learning Harmonic analysis Microphone arrays Vectors

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HDR Imaging with One-Bit Quantization

HDR Imaging with One-Bit Quantization

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sensor array and multichannel signal processing workshop

作者： Arian Eamaz Farhang Yeganegi Mojtaba Soltanalian University of Illinois Chicago Chicago IL USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Modulo sampling and dithered one-bit quantization frame-works have emerged as promising solutions to overcome the limitations of traditional analog-to-digital converters (ADCs) and sensors. Modulo sampling, with its high-resolution approach utilizing modulo ADCs, offers an unlimited dynamic range, while dithered one-bit quantization offers cost-efficiency and reduced power consumption while operating at elevated sampling rates. Our goal is to explore the synergies between these two techniques, leveraging their unique advantages, and to apply them to non-bandlimited signals within spline spaces. One noteworthy application of these signals lies in High Dynamic Range (HDR) imaging. In this paper, we expand upon the Unlimited One-Bit (UNO) sampling framework, initially conceived for bandlimited signals as proposed in [1], to encompass non-bandlimited signals found in the context of HDR imaging. We present a novel algorithm rigorously examined for its ability to recover images from one-bit modulo samples. Additionally, we introduce a sufficient condition specifically designed for UNO sampling to properly recover non-bandlimited signals within spline spaces. Our numerical results vividly demonstrate the effectiveness of UNO sampling in the realm of HDR imaging.

关键词： Image sensors Sufficient conditions Quantization (signal) Power demand Heuristic algorithms signal processing algorithms sensors

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Semi-Passive RIS-Aided Sequential Channel Estimation and Prediction

Semi-Passive RIS-Aided Sequential Channel Estimation and Pre...

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sensor array and multichannel signal processing workshop

作者： Mirza A. Haider Yimin D. Zhang Yanwu Ding Dan Shen Khanh Pham Genshe Chen Department of Electrical and Computer Engineering Temple University Philadelphia PA USA Department of Electrical and Computer Engineering Wichita State University Wichita KS USA Intelligent Fusion Technology Inc. Germantown MD USA Air Force Research Laboratory Kirtland Air Force Base NM USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

The popularity of mmWave in 5G and future communications is hindered by challenging propagation environments, such as line-of-sight obstruction. Reconfigurable intelligent surfaces (RIS) address this issue by dynamically modifying wireless channels, thereby enhancing data rates, reducing latency, and improving reliability in non-line-of-sight scenarios. For high data-rate communication and precise mobile user localization, a large RIS is required, resulting in a high pilot overhead for channel estimation. To address this issue, we exploit a semi-passive RIS with sparsely distributed active RIS elements in lieu of fully passive RIS. This approach efficiently enables channel estimation both between the base station and the RIS as well as between the RIS and the mobile users. Structured covariance matrix interpolation optimally utilizes the array aperture from the sparsely placed active RIS elements. Recognizing the need for frequent channel estimation, we introduce a recurrent neural network-based model for sequential channel prediction, resulting in a significant reduction of the required training pilot signals. Simulation results affirm the capability and effectiveness of the proposed approach to enhance data transmission.

关键词： Wireless communication Training Wireless sensor networks Machine learning algorithms array signal processing Simulation Channel estimation

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CI-Based QoS-Constrained Transmit signal Design for DFRC Systems with One-Bit DACs

CI-Based QoS-Constrained Transmit Signal Design for DFRC Sys...

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sensor array and multichannel signal processing workshop

作者： Zheyu Wu Wei-Kun Chen Ya-Feng Liu Christos Masouros LSEC ICMSEC AMSS Chinese Academy of Sciences Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China School of Mathematics and Statistics Beijing Institute of Technology Beijing China Department of Electronic and Electrical Engineering University College London London UK

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

In this paper, we investigate the transmit signal de-sign problem for a dual-functional radar-communication (DFRC) system equipped with one-bit digital-to-analog converters (DACs). Specifically, the one-bit DFRC waveform is designed to minimize the difference between the transmitted beampattern and a desired one, while ensuring constructive interference (CI)-based QoS constraints for communication users. The formulated problem is a discrete optimization problem with a nonconvex objective function and many linear constraints. To solve it, we first propose a penalty model to transform the discrete problem into a continuous one. Then, we propose an inexact augmented Lagrangian method (ALM) framework to solve the penalty model. In particular, the ALM subproblem at each iteration is solved by a custom-designed block successive upper-bound minimization (BSUM) algorithm, which admits closed-form updates and thus makes the proposed approach computationally efficient. Simulation results verify the superiority of the proposed approach over the existing ones in both the radar and communication performance.

关键词： Simulation signal processing algorithms Radar Quality of service Transforms Minimization Radar signal processing

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