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检索条件"任意字段=2008 IEEE Sensor Array and Multichannel Signal Processing Workshop"

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Comparison of Single Frame Classification with Micro-Doppler Classification of VRUs for Traffic Radar

Comparison of Single Frame Classification with Micro-Doppler...

引用

sensor array and multichannel signal processing workshop

作者： Rajab Murtaja Mouhammed Aghiad Raslan Tobias Uhlich Andreas Becker Fachhochschule Dortmund Dortmund Germany

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Protecting Vulnerable Road Users (VRUs) is a critical concern, particularly in the context of autonomous driving at intersections, where the environment becomes complex and challenging. Various considerations, including accuracy, reliability, and system processing time, are crucial for VRUs classification models. This paper aims to assess the feasibility of state-of-the-art models in real-world scenarios, comparing two VRU classification methods using a stationary radar: a single-frame approach and a multi-frame micro-doppler method. The single-frame method employs a Convolutional Neural Network (CNN) to a cropped Range-Doppler map (RD map) around the target position, while the micro-doppler classification considers both a CNN network and a CNN-LSTM network and uses the micro-doppler spectrogram as an input. Focused on pedestrians, vehicles, and cyclists. The results, validated through a 5-fold cross-validation and F1 score, highlight the superiority of the single-frame classification in real-world scenarios, offering instantaneous and effective multi-class multi-object object classification.

关键词： Accuracy Tracking Roads Radar Radar tracking Radar signal processing Convolutional neural networks

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Filtering as Rewiring for Bias Mitigation on Graphs

Filtering as Rewiring for Bias Mitigation on Graphs

引用

sensor array and multichannel signal processing workshop

作者： O. Deniz Kose Gonzalo Mateos Yanning Shen Dept. of EECS University of California Irvine Irvine USA Dept. of ECE University of Rochester Rochester USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Machine learning over graphs (MLoG) has attracted growing attention due to its effectiveness in processing relational data from complex systems such as social networks, financial markets, and the brain. However, MLoG algorithms that use the graph topology for information aggregation have been shown to amplify the already existing bias towards certain under-represented groups, often leading to discriminatory results in downstream tasks. In this context, here we consider the prob-lem of topology-induced algorithmic bias mitigation by cross-pollinating tools from MLoG and graph signal processing. Specifi-cally, we argue that application of a tunable debiasing graph filter can be reinterpreted as a graph rewiring process, thus offering an explicit handle to manipulate the utility versus topological bias tradeoff. Building on this insight, we formulate a fairness-aware network topology inference problem to obtain a rewired graph minimizing a correlation-based, unsupervised bias metric. Node classification experiments on several real-world datasets demonstrate that the proposed approach typically outperforms state-of-the-art baselines in terms of fairness metrics, and without a degradation in classification accuracy.

关键词： Machine learning algorithms Network topology Filtering Social networking (online) Prevention and mitigation Pipelines signal processing algorithms

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Distributed Sparse Subspace Clustering by K-Means Subspace Fusion

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

引用

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

引用

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

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

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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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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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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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