This paper addresses the problem of calibration for antenna arrays with multi-port polarimetric elements. Model-based arraysignalprocessing techniques require an accurate model of the complex array response and mode...
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ISBN:
(纸本)9798350344820;9798350344813
This paper addresses the problem of calibration for antenna arrays with multi-port polarimetric elements. Model-based arraysignalprocessing 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.
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 com...
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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.
Source localization from raw array data by reparameterizing the array steering vector by the source position is a fundamental principle of direct position determination (DPD). In this paper, we propose a DPD method to...
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ISBN:
(纸本)9798350344820;9798350344813
Source localization from raw array data by reparameterizing the array steering vector by the source position is a fundamental principle of direct position determination (DPD). In this paper, we propose a DPD method to estimate the position of locally scattered sources using an unsynchronized arraysensor network. Here, we use the well-known generalized array manifold (GAM) model which approximates the steering vector using its first-order gradient in order to characterize the local scattering effect. The proposed method is compared with the conventional two-stage bearings-only localization (BOL) approach. Simulation results reveal that the proposed DPD position estimates asymptotically attain the derived Cramier-Rao Bound (CRB) for high SNR values and an improved localization accuracy is achieved by exploiting the local scattering parameters in the localization.
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 ...
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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.
We estimate the radius of a perfect electric conductor (PEC) half-sphere placed on a ground plane, given six bi-conical antennas that suffer from manufacturing errors. The results are complemented by an analytical mod...
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ISBN:
(纸本)9781424422401
We estimate the radius of a perfect electric conductor (PEC) half-sphere placed on a ground plane, given six bi-conical antennas that suffer from manufacturing errors. The results are complemented by an analytical model of the sensor system and the scatterer. The analytical model includes the front-end electronics in combination with the antenna radiation impedance and the radiation field function.
We consider the use of polarization diversity for the detection, tracking and classification of scattering centers in high range resolution radar (HRR) data. Specifically, we propose a novel exploitation of polarizati...
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ISBN:
(纸本)1424403081
We consider the use of polarization diversity for the detection, tracking and classification of scattering centers in high range resolution radar (HRR) data. Specifically, we propose a novel exploitation of polarization ratios to label moving scattering features into various geometrical classes including edges, tips or smooth reflectors. We demonstrate scattering-center labeling for both parametric and non-parametric classification algorithms. Experimental results on simulated HRR data are provided and indicate that polarimetric information can be exploited for geometrical typing of scatterers.
Graph learning has been widely used in many fields to study the relationships between different entities in a dataset. We present an optimization framework based on the proximal alternating direction method of multipl...
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ISBN:
(纸本)9798350344820;9798350344813
Graph learning has been widely used in many fields to study the relationships between different entities in a dataset. We present an optimization framework based on the proximal alternating direction method of multipliers (pADMM) for learning general signed graphs from smooth signals. We show that our proposed pADMM enjoys global convergence and a local linear convergence rate. Then, we demonstrate the effectiveness of the proposed framework through numerical experiments on signed graphs. Our proposed framework provides a promising approach for learning general signed graphs from smooth signals and can be a valuable tool for data analysis and decision-making in various fields.
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 secto...
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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.
The estimation of directions of arrival is formulated as the decomposition of a 3-way array into a sum of rank-one terms. However, a low-rank tensor approximation does not always exist. We propose an optimization tech...
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ISBN:
(纸本)9781479914814
The estimation of directions of arrival is formulated as the decomposition of a 3-way array into a sum of rank-one terms. However, a low-rank tensor approximation does not always exist. We propose an optimization technique based on differentiable angular constraints on the factors, ensuring the existence of the low-rank tensor decomposition. The efficiency of the proposed algorithm is demonstrated via numerical simulations, and compared to Cramer-Rao bounds.
The application of graph signalprocessing (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...
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ISBN:
(纸本)9798350344820;9798350344813
The application of graph signalprocessing (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.
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