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In this paper, we propose an extension to the Propagator algorithm for source bearing estimation by performing root decomposition which eliminates the need for spectral search over angles. Further the propagator spatial spectrum is reused…

信号处理 · 电气工程与系统科学 2025-01-27 Pradeep Dheerendra , Zoltan Derzsi

Modulo sampling has recently drawn a great deal of attention for cutting-edge applications, due to overcoming the barrier of information loss through sensor saturation and clipping. This is a significant problem, especially when the range…

信号处理 · 电气工程与系统科学 2024-01-02 Qi Zhang , Jiang Zhu , Fengzhong Qu , De Wen Soh

This paper presents a performance analysis of the MUltiple SIgnal Classification (MUSIC) algorithm applied on $D$ dimensional single-snapshot spectral estimation while $s$ true frequencies are located on the continuum of a bounded domain.…

信息论 · 计算机科学 2016-11-17 Wenjing Liao

A new spatial IIR beamformer based direction-of-arrival (DoA) estimation method is proposed in this paper. We propose a retransmission based spatial feedback method for an array of transmit and receive antennas that improves the performance…

信号处理 · 电气工程与系统科学 2023-09-07 Parth Mehta , Kumar Appaiah , Rajbabu Velmurugan

We introduce a learning-based algorithm to obtain a measurement matrix for compressive sensing related recovery problems. The focus lies on matrices with a constant modulus constraint which typically represent a network of analog phase…

信号处理 · 电气工程与系统科学 2021-10-15 Michael Koller , Wolfgang Utschick

Spatial compressive sensing (SCS) has recently been applied to direction-of-arrival (DOA) estimation owing to advantages over conventional ones. However the performance of compressive sensing (CS)-based estimation methods decreases when…

信息论 · 计算机科学 2016-01-20 Liang Liu , Ping Wei

In this work, we explore the problems of detecting the number of narrow-band, far-field targets and estimating their corresponding directions from single snapshot measurements. The principles of sparse signal recovery (SSR) are used for the…

应用统计 · 统计学 2017-05-23 Rakshith Jagannath

In ultrasound (US) imaging, various types of adaptive beamforming techniques have been investigated to improve the resolution and contrast-to-noise ratio of the delay and sum (DAS) beamformers. Unfortunately, the performance of these…

图像与视频处理 · 电气工程与系统科学 2020-02-25 Shujaat Khan , Jaeyoung Huh , Jong Chul Ye

Snapshot mosaic multispectral imagery acquires an undersampled data cube by acquiring a single spectral measurement per spatial pixel. Sensors which acquire $p$ frequencies, therefore, suffer from severe $1/p$ undersampling of the full data…

计算机视觉与模式识别 · 计算机科学 2019-11-26 Giancarlo A. Antonucci , Simon Vary , David Humphreys , Robert A. Lamb , Jonathan Piper , Jared Tanner

Sliding mode control (SMC) is a robust and computationally efficient solution for tracking control problems of highly nonlinear systems with a great deal of uncertainty. High frequency oscillations due to chattering phenomena and…

最优化与控制 · 数学 2017-06-08 Mohammad Reza Amini , Mahdi Shahbakhti , Selina Pan , J. Karl Hedrick

Wideband spectrum sensing detects the unused spectrum holes for dynamic spectrum access (DSA). Too high sampling rate is the main problem. Compressive sensing (CS) can reconstruct sparse signal with much fewer randomized samples than…

信息论 · 计算机科学 2012-04-16 Yipeng Liu , Qun Wan

The authors recently proposed a MIMO radar system that is implemented by a small wireless network. By applying compressive sensing (CS) at the receive nodes, the MIMO radar super-resolution can be achieved with far fewer observations than…

信息论 · 计算机科学 2009-12-08 Yao Yu , Athina P. Petropulu , H. Vincent Poor

Blind source separation (BSS) algorithms are unsupervised methods, which are the cornerstone of hyperspectral data analysis by allowing for physically meaningful data decompositions. BSS problems being ill-posed, the resolution requires…

信号处理 · 电气工程与系统科学 2022-09-28 Rémi Carloni Gertosio , Jérôme Bobin , Fabio Acero

We address the problem of tensor decomposition in application to direction-of-arrival (DOA) estimation for transmit beamspace (TB) multiple-input multiple-output (MIMO) radar. A general 4-order tensor model that enables computationally…

信息论 · 计算机科学 2022-06-29 Feng Xu , Matthew W. Morency , Sergiy A. Vorobyov

In this paper, a new multi-source wideband direction of arrival (MSW-DOA) estimation method is proposed for the signal with non-uniform distribution using the sub-array of uniform linear array. Different from conventional methods, based on…

音频与语音处理 · 电气工程与系统科学 2022-04-12 Jing Zhou , Changchun Bao

We consider the problem of direction-of-arrival (DOA) estimation in unknown partially correlated noise environments where the noise covariance matrix is sparse. A sparse noise covariance matrix is a common model for a sparse array of…

Recent neural network based Direction of Arrival (DoA) estimation algorithms have performed well on unknown number of sound sources scenarios. These algorithms are usually achieved by mapping the multi-channel audio input to the single…

音频与语音处理 · 电气工程与系统科学 2022-11-18 Haoran Yin , Meng Ge , Yanjie Fu , Gaoyan Zhang , Longbiao Wang , Lei Zhang , Lin Qiu , Jianwu Dang

Compressed sensing (CS)-based techniques have been widely applied in the grant-free non-orthogonal multiple access (NOMA) to a single-antenna base station (BS). In this paper, we consider the multi-antenna reception at the BS for uplink…

信号处理 · 电气工程与系统科学 2025-03-11 Guoqing Xia , Pei Xiao , Bohan Li , Yue Zhang , Huiyu Zhou

The direction of arrival (DOA) estimation algorithms are crucial in localizing acoustic sources. Traditional localization methods rely on block-level processing to extract the directional information from multiple measurements processed…

音频与语音处理 · 电气工程与系统科学 2023-08-15 Ruchi Pandey , Santosh Nannuru

In colocated multiple-input multiple-output (MIMO) radar using compressive sensing (CS), a receive node compresses its received signal via a linear transformation, referred to as measurement matrix. The samples are subsequently forwarded to…

信息论 · 计算机科学 2015-05-27 Y. Yu , A. P. Petropulu , H. V. Poor