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检索条件"任意字段=12th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2022"

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Decentralized Online Direction-of-Arrival Estimation and Tracking 12

Decentralized Online Direction-of-Arrival Estimation and Tra...

引用

ieee 12th sensor array and multichannel signal processing workshop (sam)

作者： Fan, Yufan Ardic, Cemil Emre Minh Trinh-Hoang Pesavento, Marius Tech Univ Darmstadt Commun Syst Grp Darmstadt Germany

ISBN: (纸本)9781665406338

the problem of the decentralized Direction-ofArrival (DoA) estimation and tracking is addressed, where the sample covariance matrix is represented as a recursive update of rank-one components. Due to the fact that the conventional decentralized power method is a batch approach and involves high communication costs, we propose a distributed DoA estimation method combining the decentralized online eigenvalue decomposition and the ESPRIT algorithm. the Push-Sum protocol is applied to realize the local interactions among neighboring subarrays. the simulation results show that the Root Mean Square Error (RMSE) performance of our distributed algorithm surpasses that of the power method based d-ESPRIT algorithm in the DoA estimation with lower total communication cost, and that of the decentralized Normalized Oja (d-NOja) method in DoA tracking applications with faster convergence speed.

关键词： Direction-of-arrival estimation Costs Protocols Simulation signal processing algorithms Estimation Eigenvalues and eigenfunctions

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Sparse Channel Estimation for IRS-Aided Systems Exploiting 2-D Sparse arrays 12

Sparse Channel Estimation for IRS-Aided Systems Exploiting 2...

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ieee 12th sensor array and multichannel signal processing workshop (sam)

作者： Haider, Mirza Asif Chowdhury, Md. Waqeeb T. S. Zhang, Yimin D. Temple Univ Dept Elect & Comp Engn Philadelphia PA 19122 USA

ISBN: (纸本)9781665406338

Intelligent reflecting surface (IRS) is a promising next-generation technology for increasing channel capacity and reducing power consumption. In this paper, we present a novel IRS configuration consisting of a small number of active elements in an optimized L-shaped sparse array to separately estimate the channels between the base station and the IRS, and the channel between multiple user equipment and the IRS. Structured matrix completion techniques are used to attain superior direction-ofarrival estimation performance with an increased number of degrees of freedom. the training overhead is minimized in the proposed system and is not directly related to the number of IRS reflecting elements. the proposed sparse array strategy simultaneously resolves multiple sources with a high accuracy and outperforms the L-shaped uniform array counterpart using the same number of active elements. the effectiveness of the proposed strategy is confirmed using simulation results.

关键词： Intelligent reflecting surface active elements sparse array structured matrix completion channel estimation

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UAV-BASED URBAN MONITORING USING ON-BOARD 802.11AD RADAR 12

UAV-BASED URBAN MONITORING USING ON-BOARD 802.11AD RADAR

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ieee 12th sensor array and multichannel signal processing workshop (sam)

作者： Ram, Shobha Sundar Mishra, Kumar Vijay Indraprastha Inst Informat Technol Delhi New Delhi 110020 India United States DEVCOM Army Res Lab Adelphi MD 20783 USA

ISBN: (纸本)9781665406338

the unmanned aerial vehicles (UAVs) offer low-cost, flexible, and line-of-sight deployment for radar remote sensing as well as building temporary communications base stations in inaccessible or crowded areas. their limited battery power and small footprints is driving innovations to combine multiple functions in single hardware operating in the same spectrum. In particular, UAV-borne joint radarcommunications (JRC) systems are of significant interest. In this paper, we focus on a wide-bandwidth millimeter-wave JRC system for UAVs that employ ieee 802.11ad protocol for sensing. We model such a system for detecting dynamic users on the ground such as pedestrians and vehicles. Our method incorporates a realistic electromagnetic scattering center model of road targets and surface clutter in the pyBullet software to examine 802.11ad for UAV-borne sensing. Numerical experiments demonstrate that 802.11ad UAVborne sensing is able to capture several micro-Doppler characteristics.

关键词： Beam positioning ieee 802.11ad joint radarcommunications micro-Doppler UAV base station.

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Dynamic TDD Enabled Distributed Antenna array Massive MIMO System 12

Dynamic TDD Enabled Distributed Antenna Array Massive MIMO S...

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ieee 12th sensor array and multichannel signal processing workshop (sam)

作者： Chowdhury, Anubhab Murthy, Chandra R. Chopra, Ribhu Indian Inst Sci Dept ECE Bangalore Karnataka India Indian Inst Technol Guwahati Dept EEE Gauhati India

ISBN: (纸本)9781665406338

In this paper, we analyze the performance of dynamic time division duplexing (DTDD) in a distributed antenna array massive multiple-input multiple-output (DAA-mMIMO) system in the presence of simultaneous uplink (UL) and downlink (DL) data requirements. DTDD has been shown to substantially improve system performance in terms of spectral efficiency (SE) for cellular mMIMO systems. In the DAAenabled system, we show that the performance of DTDD depends critically on appropriately scheduling the UL/DL modes at the access points (APs) based on the localized UL/DL traffic demand. Since an exhaustive search over all possible UL/DL modes is combinatorially complex, we present a greedy algorithm to solve the AP-scheduling problem in polynomial time. Via extensive numerical experiments, we illustrate the 95%-likely sum UL-DL SE achieved by our solution, and show that DAA-mMIMO with dynamic AP-scheduling can substantially outperform TDD-based cellular as well as DAA mMIMO systems.

关键词： Distributed antenna arrays dynamic TDD distributed processing massive MIMO

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DETECTION OF FALSE DATA INJECTION ATTACKS IN UNOBSERVABLE POWER SYSTEMS BY LAPLACIAN REGULARIZATION 12

DETECTION OF FALSE DATA INJECTION ATTACKS IN UNOBSERVABLE PO...

引用

ieee 12th sensor array and multichannel signal processing workshop (sam)

作者： Dabush, Lital Routtenberg, Tirza Ben Gurion Univ Negev Sch Elect & Comp Engn Beer Sheva Israel

ISBN: (纸本)9781665406338

the modern electrical grid is a complex cyber-physical system, and thus is vulnerable to measurement losses and attacks. In this paper, we consider the problem of detecting false data injection (FDI) attacks and bad data in unobservable power systems. Classical bad-data detection methods usually assume observable systems and cannot detect stealth FDI attacks. We use the smoothness property of the system states (voltages) w.r.t. the admittance matrix, which is also the Laplacian of the graph representation of the grid. First, we present the Laplacian-based regularized state estimator, which does not require full observability of the network. then, we derive the Laplacian-regularized generalized likelihood ratio test (LR-GLRT). We show that the LR-GLRT has a component of a soft high-pass graph filter applied to the state estimator. Numerical results on the ieee 118-bus system demonstrate that the LR-GLRT outperforms other detection approaches and is robust to missing data.

关键词： Graph signal processing (GSP) bad-data detection false data injection (FDI) attacks power system state estimation (PSSE) attack detection

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Wireless Inference Gets Smarter: RIS-assisted Channel-Aware MIMO Decision Fusion 12

Wireless Inference Gets Smarter: RIS-assisted Channel-Aware ...

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ieee 12th sensor array and multichannel signal processing workshop (sam)

作者： Mudkey, Nishanth Ciuonzo, Domenico Zappone, Alessio Rossi, Pierluigi Salvo Stevens Inst Technol Hoboken NJ 07030 USA Univ Naples Federico II Dept Elect Engn & Informat Technol DIETI Naples Italy Univ Cassino & Southern Lazio Dept Elect & Informat Engn Maurizio Scarano Cassino Italy Norwegian Univ Sci & Technol Dept Elect Syst Trondheim Norway Consorzio Nazl Interuniv Telecomunicaz CNIT Parma Italy SINTEF Energy Res Dept Gas Technol Trondheim Norway

ISBN: (纸本)9781665406338

We study channel-aware binary-decision fusion over a shared flat-fading channel with multiple antennas at the Fusion Center (FC). this paper considers the aid of a Reconfigurable Intelligent Surface (RIS) to effectively convey the information of the phenomenon of interest to the FC and foster energyefficient data analytics supporting the Internet of things (IoT) paradigm. We present the optimal rule and derive a (sub-optimal) joint fusion rule & RIS design, representing an alternative with reduced complexity and lower system knowledge required. Simulation results for performance are presented showing the benefit of RIS adoption even in a suboptimal case.

关键词： Decision Fusion Internet of things Reconfigurable Intelligent Surface Wireless sensor Networks

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Compound Interference Suppression for Bistatic FDA-MIMO Radar Based on Joint Two-Stage processing 12

Compound Interference Suppression for Bistatic FDA-MIMO Rada...

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ieee 12th sensor array and multichannel signal processing workshop (sam)

作者： Sun, Wenhao Lan, Lan Liao, Guisheng Qi, Jiawei Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

ISBN: (纸本)9781665406338

Frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radar is characterized by extra degreesof-freedom (DOFs) in the range domain, which can be utilized in mainlobe deceptive jammer suppression. However, the jammer suppression performance degrades evidently in the presence of compound suppressive jamming signals and deceptive jammers. To this end, this paper deals with the suppression of compound interferences (including the suppressive jamming signals and deceptive jammers) in bistatic FDA-MIMO radar. At the design stage, a colocated FDA-MIMO radar is considered in node 1, while the node 2 serves as a receive-only array, which is configured as a distributed FDA-MIMO radar by combining with the transmit array in node 1. then, a two-stage processing is performed to suppress the compound interferences, including: 1) the mainlobe maintenance processing to ensure the maximum response of the target;2) the suppression of mainlobe interferences with joint local and global processing. At the analysis stage, the output signal-to-interference-plus-noise ratio (SINR) is analyzed, and a comparison among different radar frameworks is carried out. Numerical results demonstrate the effectiveness of the proposed method in suppressing the compound interference.

关键词： Bistatic FDA-MIMO radar two-stage processing mainlobe interference suppression output SINR

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OPTIMAL ANGULAR sensor SEPARATION FOR DRSS LOCALIZATION 12

OPTIMAL ANGULAR SENSOR SEPARATION FOR DRSS LOCALIZATION

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ieee 12th sensor array and multichannel signal processing workshop (sam)

作者： Li, Jun Dogancay, Kutluyil Hmam, Hatem Univ South Australia UniSA STEM Mawson Lakes Australia Def Sci & Technol Grp Cyber & Elect Warfare Div Canberra ACT Australia

ISBN: (纸本)9781665406338

this paper investigates optimal target-sensor geometries for differential received signal strength (DRSS) localization in the 2D-plane. To achieve this, theD-optimality criterion is employed, whichmaximizes the determinant of the Fisher information matrix (FIM). We observe that equiangular sensor separationmaximizes the determinant of the FIM, forming the best geometry, for equal target-sensor ranges under the assumption of identical log-normal noise variances for all sensor measurements. However, when the distances between the target and sensors are different, equiangular sensor separation cannot meet the optimal geometry condition. We provide analytical and numerical solutions for optimal geometries for the specific cases of three and four sensors with non-uniform target-sensor ranges.

关键词： differential received signal strength D-optimality criterion Fisher information matrix equiangular sensor separation

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STATISTICAL ANALYSES OF MEASURED FORWARD-LOOKING SONAR ECHO DATA IN A SHALLOWWATER ENVIRONMENT 12

STATISTICAL ANALYSES OF MEASURED FORWARD-LOOKING SONAR ECHO ...

引用

ieee 12th sensor array and multichannel signal processing workshop (sam)

作者： Shen, Jiajun Gini, Fulvio Greco, Maria Sabrina Zhou, Tian Harbin Engn Univ Coll Underwater Acoust Engn Harbin 150001 Peoples R China Univ Pisa Dept Informat Engn I-56122 Pisa Italy Peng Cheng Lab Shenzhen 518055 Peoples R China

ISBN: (纸本)9781665406338

the performance of underwater target detection significantly depends on the statistical distribution of reverberation and knowledge of data correlation for active sonar. To reasonably design constant false alarm rate (CFAR) detectors that exploit knowledge of reverberation characteristics, a preliminary statistical analysis of active sonar echo data is necessary. In this study, we analyze the amplitude statistics of the measured forward-looking sonar echo data after applying the beamforming processing with a variety of distributions including log-normal, Weibull, Rayleigh, K, generalized Pareto (GP) and inverse Gaussian distributions. the results show that for the high-frequency sonar working in shallow water, both the K and the GP distributions can provide a good data fitting, while the GP distribution performs better especially in the tail region of the distribution. At last, the spatio-temporal correlation of data analysis and concluding remarks complete this paper.

关键词： Correlation array signal processing Statistical analysis Sonar measurements Sonar Sea measurements Tail

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Guaranteed Deep Learning for Reliable Radar signal processing 12

Guaranteed Deep Learning for Reliable Radar Signal Processin...

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ieee 12th sensor array and multichannel signal processing workshop (sam)

作者： Khobahi, Shahin Mostajeran, Ali Emadi, Mohammad Wang, Pu Soltanalian, Mojtaba Univ Illinois Chicago IL 60607 USA Zadar Labs Inc Santa Clara CA USA Mitsubishi Elect Res Labs Cambridge MA USA

ISBN: (纸本)9781665406338

Recently, there has been a significant level of attention paid to the application of deep learning in radar signal processing. Despite its flexibility, deep learning imposes new challenges in guaranteeing the performance of signal processing systems and in establishing trust with regard to their outcome. this represents a critical bottleneck in the application of deep learning in radar signal processing, where having trust in radar inferences is crucial. In this work, we present a novel implicit deep learning model for learning fast and highly-scalable solvers for a general family of optimization problems commonly encountered in the radar signal processing area, encompassing applications in waveform design and target parameter estimation. Specifically, the proposed methodology is based on generalizing the feed-forward neural networks by introducing implicit layers derived from the dynamics of a fixed-point geometric series. Unlike its black-box data-driven counterparts, the implicit and model-based nature of the proposed neural solver significantly reduces the parameters of the network and implementation cost, and most importantly, makes the network amenable to robustness analysis, as well as deriving performance guarantees/bounds on the output of the model. this presents considerable potential for the adoption of our proposed models in radar applications, be it in the classical settings or emerging applications in autonomous vehicles and automotive radar where a large number of reliable radars are projected to be on the road in the near future.

关键词： Implicit Deep Learning Inference Guarantees Model-Based Deep Learning Radar signal processing Robustness and Reliability

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