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共 1267 条记录，以下是11-20 订阅

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Calibration of Polarimetric Antenna arrays Using Neural Networks 13

Calibration of Polarimetric Antenna Arrays Using Neural Netw...

引用

13rd ieee sensor array and multichannel signal processing workshop (SAM)

作者： Springer, Jannik Mikus, Philipp Oispuu, Marc Koch, Wolfgang Knott, Peter Fraunhofer Inst Commun Informat Proc & Ergon FKIE Fraunhoferstr 20 D-53343 Wachtberg Germany Fraunhofer Inst High Frequency Phys & Radar Tech Fraunhoferstr 20 D-53343 Wachtberg Germany

ISBN: (纸本)9798350344820;9798350344813

This paper addresses the problem of calibration for antenna arrays with multi-port polarimetric elements. Model-based array signal processing techniques require an accurate model of the complex array response and model errors can, for example, cause significant systematic direction finding errors. Modeling the response of polarimetric antenna arrays can be particularly challenging due to cross-polarization and mutual coupling effects in the multi-port antenna elements. This work proposes a new calibration technique for polarimetric antenna arrays using neural networks that learn any mismatches between the modeled and the actual array response. The technique is evaluated based on the measured response of a five-element dual-polarized antenna array and outperforms conventional calibration techniques like mutual coupling calibration or local polynomial approximation. Its performance is studied exemplarily for the direction finding problem.

关键词： antenna array polarization calibration machine learning neural network direction finding

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Blind Phase-Offset Estimation in Sparse Partly Calibrated arrays 13

Blind Phase-Offset Estimation in Sparse Partly Calibrated Ar...

引用

13rd ieee sensor array and multichannel signal processing workshop (SAM)

作者： Liu, Tianyi Pesavento, Marius Tech Univ Darmstadt Commun Syst Grp Darmstadt Germany

ISBN: (纸本)9798350344820;9798350344813

Spatial frequency estimation from a superposition of impinging waveforms in the presence of noise is important in many applications. While subspace-based methods offer high-resolution parameter estimation at a low computational cost, they heavily rely on precise array calibration with a synchronized clock, posing challenges for large distributed antenna arrays. In this study, we focus on direction-of-arrival (DoA) estimation within sparse partly calibrated rectangular arrays. These arrays consist of multiple perfectly calibrated subarrays with unknown phase-offsets among them. We present a gridless sparse formulation for DoA estimation leveraging the multiple shift-invariance properties in the partly calibrated array. Additionally, an efficient blind calibration technique is proposed based on semidefinite relaxation to estimate the intersubarray phase-offsets accurately.

关键词： DoA estimation sparse arrays thinned arrays joint sparsity partly calibrated arrays shift-invariance blind array calibration

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Receiver Antenna Allocation for Joint Sensing and Communications 13

Receiver Antenna Allocation for Joint Sensing and Communicat...

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13rd ieee sensor array and multichannel signal processing workshop (SAM)

作者： van der Werf, Ids Leus, Geert Chepuri, Sundeep Prabhakar Delft Univ Technol Delft Netherlands Indian Inst Sci Bengaluru India

ISBN: (纸本)9798350344820;9798350344813

We describe the problem of joint sensing and communications (JSAC) using an antenna array where some antennas are used for sensing and some others for communications. The problem is to jointly optimize the sensing and communications performance at the receive side through respectively the CramerRao bound (CRB) on the source angles and the variance of the sources after applying a minimum variance distortionless response (MVDR) beamformer. Both criteria are derived under a two-source scenario and optimized for any position of the two sources. Here one of the sources is considered the source of interest, while the other one models the interference. The performance criteria for sensing and communications are mathematically similar and depend only on the angular difference between the two sources. As a result, they can be elegantly combined and jointly optimized. Our findings are corroborated by simulations under different parameter settings.

关键词： Convex optimization Cramer-Rao bound sensor selection joint sensing and communication multi-target estimation

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SPARSE array AND PRECODING DESIGN FOR INTEGRATED SENSING AND COMMUNICATION SYSTEMS 13

SPARSE ARRAY AND PRECODING DESIGN FOR INTEGRATED SENSING AND...

引用

13rd ieee sensor array and multichannel signal processing workshop (SAM)

作者： Sankar, R. S. Prasobh Chepuri, Sundeep Prabhakar Indian Inst Sci Bengaluru Karnataka India

ISBN: (纸本)9798350344820;9798350344813

In this paper, we perform joint antenna selection and transmit pre-coder design for integrated sensing and communication (ISAC) systems to meet signal-to-interference-plus-noise-ratio (SINR) requirements at the users while being capable of identifying, i.e., estimating the direction of arrivals (DoAs), of certain number of sources. We first present a sufficient condition to ensure certain identifiability. Next, through a series of relaxations, we obtain a convex approximation to the combinatorial antenna selection and precoding problem, which we solve using off-the-shelf solvers. The proposed method offers comparable performance to ISAC systems with optimal antenna selection obtained through exhaustive search while significantly out-performing ISAC systems with arbitrarily selected active antennas.

关键词： Antenna selection beamforming integrated sensing and communication MIMO systems sparse arrays

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On Detecting Low-pass Graph signals under Partial Observations 13

On Detecting Low-pass Graph Signals under Partial Observatio...

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13rd ieee sensor array and multichannel signal processing workshop (SAM)

作者： Nguyen, Hoang-Son Wai, Hoi-To Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China

ISBN: (纸本)9798350344820;9798350344813

The application of graph signal processing (GSP) on partially observed graph signals with missing nodes has gained attention recently. This is because processing data from large graphs are difficult, if not impossible due to the lack of availability of full observations. Many prior works have been developed using the assumption that the generated graph signals are smooth or low pass filtered. This paper treats a blind graph filter detection problem under this context. We propose a detector that certifies whether the partially observed graph signals are low pass filtered, without requiring the graph topology knowledge. As an example application, our detector leads to a pre-screening method to filter out non low pass signals and thus robustify the prior GSP algorithms. We also bound the sample complexity of our detector in terms of the class of filters, number of observed nodes, etc. Numerical experiments verify the efficacy of our method.

关键词： graph signal processing low pass graph filter partial observations

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Low Complexity Beam Domain processing for Autoencoder Based CSI Compression 13

Low Complexity Beam Domain Processing for Autoencoder Based ...

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13rd ieee sensor array and multichannel signal processing workshop (SAM)

作者： Ibrahim, Mohamed Salah Malhotra, Akshay InterDigital Commun Wilmington DE 19809 USA

ISBN: (纸本)9798350344820;9798350344813

In frequency division duplex (FDD) massive MIMO systems, each user equipment (UE) device typically feeds back downlink channel state information (CSI) to its serving base station (BS), which may then be utilized for beamforming and scheduling purposes. However, the large number of antennas increases the CSI signaling overhead in the uplink, thereby impacting the system's spectral efficiency. To address the feedback overhead problem, this paper proposes a low-complexity beam domain autoencoder that operates on low-dimensional beam domain channel samples. The key idea is to efficiently transform the channel from the antenna-domain to the beam-domain by selecting a small number of beams that preserve most of the channel information. To leverage the beam domain processor, we propose a beam selection method that enables high reconstruction quality with a small number of selected beams. The proposed framework offers a favorable balance between complexity, performance, and memory requirements. Simulations demonstrate the efficacy of the proposed approach in recovering the compressed channel at reduced overhead and complexity, relative to the state-of-the-art methods.

关键词： Frequency division multiplexing

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Experimental Evaluation of a Null-Steered Performance Weighted Blended Beamformer 13

Experimental Evaluation of a Null-Steered Performance Weight...

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13rd ieee sensor array and multichannel signal processing workshop (SAM)

作者： Tucker, Jeff Wage, Kathleen E. George Mason Univ ECE Dept Fairfax VA 22030 USA

ISBN: (纸本)9798350344820;9798350344813

In array signal processing beamformers are used to improve the signal to interference and noise ratio (SINR). Dominant mode rejection (DMR) is a common adaptive beamforming algorithm designed to detect quiet sources in the presence of a few loud interferers. Buck and Singer proposed the Universal DMR (UDMR) beamformer that automates the selection of the subspace rank by blending fixed-rank DMR beamformers based on their performance. While UDMR requires only slightly more computation than a single fixed-rank DMR, it still requires a computationally expensive eigendecomposition. This paper proposes to reduce the computational complexity of the UDMR by replacing the DMR beamformers with a set of conventional beamformers. The null-steered performance weighted blended (NS-PWB) beamformer combines beamformers with nulls steered to interlocking locations so that if a planewave interferer is present at least one beamformer in the set will have a null nearby. Additionally, when there are persistent sources of non-plane-wave interference then beamformers can be designed and included in the beamformer set to reject those interferers when they are present. This paper applies the NS-PWB beamformer to data from a vertical line array in the Philippine Sea. The results show that the NS-PWB approximates the SINR performance of DMR beamformers with significant computational savings.

关键词： signal processing sensor arrays passive sonar universal beamforming beamforming

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Near or far: On determining the appropriate channel estimation strategy in cross-field communication 13

Near or far: On determining the appropriate channel estimati...

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13rd ieee sensor array and multichannel signal processing workshop (SAM)

作者： Tarboush, Simon Ali, Anum Al-Naffouri, Tareq Y. Samsung Res Amer Stand & Mobil Innovat Lab Plano TX USA King Abdullah Univ Sci & Technol Dept CEMSE Informat Sci Lab Thuwal Saudi Arabia

ISBN: (纸本)9798350344820;9798350344813

The use of ultra-massive multiple-input multiple-output and high-frequency large bandwidth systems is likely in the next-generation wireless communication systems. In such systems, the user moves between near- and far-field regions, and consequently, the channel estimation will need to be carried out in the cross-field scenario. Channel estimation strategies have been proposed for both near- and far-fields, but in the cross-field problem, the first step is to determine whether the near- or far-field is applicable so that an appropriate channel estimation strategy can be employed. In this work, we propose using a hidden Markov model over an ensemble of region estimates to enhance the accuracy of selecting the actual region. The region indicators are calculated using the pair-wise power differences between received signals across the subarrays within an array-of-subarrays architecture. Numerical results show that the proposed method achieves a high success rate in determining the appropriate channel estimation strategy.

关键词： array-of-subarrays spherical wave model hybrid spherical-planar wave model planar wave model

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Enhanced Jamming Suppression in Colocated MIMO Radar with Fluid Antenna array 13

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

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13rd ieee sensor array and multichannel signal processing workshop (SAM)

作者： Wu, Linlong Shankar, Bhavani M. R. Liu, Wei Ottersten, Bjorn Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT Luxembourg Luxembourg Queen Mary Univ London Sch Elect Engn & Comp Sci London England

ISBN: (纸本)9798350344820;9798350344813

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 APVs 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 Fluid antenna array waveform design jamming suppression SINR maximization

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A Machine-Learning-based approach to Direction-of-arrival Sectorization using Spherical Microphone array 13

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

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13rd ieee sensor array and multichannel signal processing workshop (SAM)

作者： Nnonyelu, Chibuzo Joseph Jiang, Meng Adamopoulou, Marianthi Lundgren, Jan Mid Sweden Univ STC Res Ctr Sundsvall Sweden

ISBN: (纸本)9798350344820;9798350344813

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： Spherical array direction of arrival estimation spherical harmonics machine learning

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