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89 篇 会议

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检索条件"任意字段=13rd IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2024"

共 89 条记录，以下是21-30 订阅

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Identical Partitioning of Consecutive Integer Set

Identical Partitioning of Consecutive Integer Set

引用

sensor array and multichannel signal processing workshop

作者： Yimin D. Zhang Shunqiao Sun Department of Electrical and Computer Engineering Temple University Philadelphia PA USA Department of Electrical and Computer Engineering The University of Alabama Tuscaloosa AL USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

In this paper, we develop a scheme to partition a one- or multi-dimensional consecutive integer number set into multiple identical, possibly rotated, subsets. The proposed technique first exploits one-dimensional nested subsets, and the results are extended to achieve two- and multi-subset partitioning as well as in two- and multi-dimensional spaces. The number of consecutive lags in each case is examined. The results are useful to various sensing and communication applications, and sparse step-frequency waveform design for range estimation in automotive radar is demonstrated as an example.

关键词： array signal processing Conferences Estimation Radar signal processing sensors Radar applications sensor arrays

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Subspace Tracking with Dynamical Models on the Grassmannian

Subspace Tracking with Dynamical Models on the Grassmannian

引用

sensor array and multichannel signal processing workshop

作者： Alex Saad-Falcon Brighton Ancelin Justin Romberg Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Tracking signals in dynamic environments presents difficulties in both analysis and implementation. In this work, we expand on a class of subspace tracking algorithms which utilize the Grassmann manifold - the set of linear subspaces of a high-dimensional vector space. We design regularized least squares algorithms based on common manifold operations and intuitive dynamical models. We demonstrate the efficacy of the approach for a narrowband beamforming scenario, where the dynamics of multiple signals of interest are captured by motion on the Grassmannian.

关键词： Manifolds array signal processing Tracking Heuristic algorithms Computational modeling signal processing algorithms Vectors

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Analysis of a Fixed Point Iteration Algorithm for TOA Localization

Analysis of a Fixed Point Iteration Algorithm for TOA Locali...

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sensor array and multichannel signal processing workshop

作者： Yanbin Zou Yangpeng Xiao Huaping Liu Department of EE Shantou University Shantou Guangdong China School of EECS Oregon State University Corvallis OR U.S.A.

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

A recent work on a fixed point iteration (FPI) al-gorithm for time-of-arrival localization has shown great promise for lower implementation complexity than existing algorithms without compromising on performance. However, this work did not provide a theoretical analysis. In the current paper, we pro-vide a comprehensive theoretical analysis of the convergence and performance of the FPI algorithm. These analyses reveal several useful conclusions about the characteristics of the estimator.

关键词： Location awareness Maximum likelihood estimation array signal processing Conferences Noise signal processing algorithms Complexity theory

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A Decentralised Asynchronous Optimisation Algorithm with an Application to Phase Retrieval

A Decentralised Asynchronous Optimisation Algorithm with an ...

引用

sensor array and multichannel signal processing workshop

作者： Behnam Mafakheri Jonathan H. Manton Iman Shames Electrical and Electronic Engineering University of Melbourne Melbrourne Australia School of Engineering Australian National University Canberra Australia

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

This paper tackles the challenge of decentralised, nonconvex optimisation in situations where agents work asynchronously. Our main contribution is a new algorithm, partially asynchronous ADMM, designed to solve decentralised optimisation problems like phase retrieval. Importantly, it does not require a central coordinator and can work with arbitrary connected network setups. We also prove that our algorithm is equivalent to the randomised block coordinate Douglas-Rachford Splitting method. To illustrate the algorithm's effectiveness, we provide numerical results for the distributed phase retrieval problem, demonstrating its correctness and performance.

关键词： array signal processing Conferences signal processing algorithms Performance analysis Optimization sensor arrays

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DynaPA: Dynamic Power Allocation for Improved Exploration-Exploitation in Active Sensing

DynaPA: Dynamic Power Allocation for Improved Exploration-Ex...

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sensor array and multichannel signal processing workshop

作者： Parthasarathi Khirwadkar Mehmet Can Hücümenoğlu Piya Pal University of California San Diego

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

This paper addresses an adaptive active sensing problem of nonstationary sparse multi-target detection, by dynamically shaping the transmit beampattern of a MIMO radar. In order to strike a better balance between exploration and exploitation (two key ideas in any online decision making problem), a dynamic power allocation algorithm called DynaPA is developed that can redirect exploration energy for exploitation (and vice versa) depending on the environment. Numerical results demonstrate superior performance of DynaPA over existing algorithms with varying target numbers.

关键词： Target tracking array signal processing Heuristic algorithms MIMO radar signal processing algorithms Dynamic scheduling Radar tracking

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Source Number Estimation for Iterative Coarray Beamforming with Partially Augmentable arrays

Source Number Estimation for Iterative Coarray Beamforming w...

引用

sensor array and multichannel signal processing workshop

作者： Fauzia Ahmad Jannatul Ferdous Department of Electrical and Computer Engineering Temple University Philadelphia PA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

In this paper, we present an iterative coarray domain beamforming algorithm with source number estimation for direction finding using partially augmentable sparse arrays, whose coarrays are non-uniform. Specifically, we employ a variant of the fast iterative interpolated beamforming (FIIB) that estimates the contributions of the missing coarray elements to emulate a uniform coarray. We exploit the deterministic nature of the noise in the coarray domain, together with its presence only at the central coarray element, to efficiently incorporate source number estimation in the FIIB algorithm. Using simulated data with a co-prime array, we demonstrate that the proposed algorithm is able to provide accurate estimates of the number of sources with high probability.

关键词： Measurement Direction-of-arrival estimation Accuracy array signal processing Conferences Noise Estimation

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A Graph Autoencoder Approach to Crowdsourcing

A Graph Autoencoder Approach to Crowdsourcing

引用

sensor array and multichannel signal processing workshop

作者： Panagiotis A. Traganitis Charilaos I. Kanatsoulis Dept. of Electrical and Computer Engineering Michigan State University East Lansing MI USA Dept. of Computer Science Stanford University Palo Alto CA USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Crowdsourcing deals with combining and aggregating labels from crowds of annotators of unknown reliability. While most works on label aggregation operate under the assumption of independent and identically distributed data, the present work introduces an algorithm that operates under known data dependencies or correlations. To exploit these dependencies, a novel graph autoencoder-based algorithm is developed that fuses annotator labels for crowdsourced classification tasks. Numerical tests on real data showcase the potential of the proposed approach.

关键词： Crowdsourcing Correlation Fuses Conferences signal processing algorithms Distributed databases Classification algorithms

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Atomic Norm Denoising for Multi-Frequency-Snapshot Doa Estimation

Atomic Norm Denoising for Multi-Frequency-Snapshot Doa Estim...

引用

sensor array and multichannel signal processing workshop

作者： Yongsung Park Peter Gerstoft Yifan Wu Michael B. Wakin NoiseLab University of California San Diego La Jolla CA USA Colorado School of Mines Golden CO USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Motivated by atomic norms in gridless sparse signal recovery’ we introduce a gridless direction-of-arrival (DOA) estimator capable of handling scenarios with multiple frequencies and multiple snapshots. Within the atomic norm frame-work, we consider a multi-frequency model analogous to a multi-snapshot model. Subsequently, we extend the model to accommodate both multi-snapshot and multi-frequency data. The proposed method is illustrated through numerical simulations and validated using ocean acoustic experimental data.

关键词： Time-frequency analysis Direction-of-arrival estimation array signal processing Estimation signal processing algorithms Frequency estimation Data models

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Revisiting semi-supervised training objectives for differentiable particle filters

Revisiting semi-supervised training objectives for different...

引用

sensor array and multichannel signal processing workshop

作者： Jiaxi Li John-Joseph Brady Xiongjie Chen Yunpeng Li Computer Science Research Centre University of Surrey Guildford United Kingdom

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Differentiable particle filters combine the flexibility of neural networks with the probabilistic nature of sequential Monte Carlo methods. However, traditional approaches rely on the availability of labelled data, i.e., the ground truth latent state information, which is often difficult to obtain in real-world applications. This paper compares the effectiveness of two semi-supervised training objectives for differentiable particle filters. We present results in two simulated environments where labelled data are scarce.

关键词： Training Monte Carlo methods Conferences Neural networks signal processing algorithms Performance gain Probabilistic logic

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Linear Convergence of Iteratively Reweighted Least Squares for Nuclear Norm Minimization

Linear Convergence of Iteratively Reweighted Least Squares f...

引用

sensor array and multichannel signal processing workshop

作者： Christian Kümmerle Dominik Stöger Department of Computer Science University of North Carolina at Charlotte Charlotte NC USA Department of Mathematics KU Eichstätt-Ingolstadt Ingolstadt Germany

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Low-rank matrix recovery problems are ubiquitous in many areas of science and engineering. One approach to solve these problems is Nuclear Norm Minimization, which is itself computationally challenging to solve. Iteratively Reweighted Least Squares (IRLS) uses a sequence of suitable (re-)weighted least squares problems to minimize the nuclear norm. However, while global convergence guarantees have been established for IRLS in this context, no convergence rates have been known so far. In this paper, we show that an IRLS variant named MatrixIRLS converges to the ground truth solution with a linear rate. Numerical simulations corroborate our theoretical findings.

关键词： array signal processing Conferences Minimization Numerical simulation Convergence sensor arrays

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