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:
(数字)9798350344813
ISBN:
(纸本)9798350344820
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 sub arrays 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.
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. Itera...
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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.
Sensing and communication systems commonly employ quadrature demodulation for signal down-conversion to baseband, using two orthogonal sinusoidal signals–an approach well-suited for systems equipped with high-resolut...
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ISBN:
(数字)9798350344813
ISBN:
(纸本)9798350344820
Sensing and communication systems commonly employ quadrature demodulation for signal down-conversion to baseband, using two orthogonal sinusoidal signals–an approach well-suited for systems equipped with high-resolution Analog-to-Digital Converters (ADCs). However, the performance of this approach is compromised when constrained to one-bit resolution ADCs. Motivated by this fact, in this paper, we explore the problem of one-bit DoA estimation, where the received signal is deliberately overdemodulated using multiple offset sinusoidal signals in the coherent analog down conversion before being processed by a one-bit ADC. Through numerical analysis, we reveal that the overdemodulation technique substantially enhances the accuracy of one-bit DoA estimation.
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...
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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.
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....
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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.
In this paper we develop a novel learning-based approach for mobile distributed beamforming without channel state information. We consider narrowband beamforming between a mobile UAV group and a base station under lim...
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ISBN:
(数字)9798350344813
ISBN:
(纸本)9798350344820
In this paper we develop a novel learning-based approach for mobile distributed beamforming without channel state information. We consider narrowband beamforming between a mobile UAV group and a base station under limited feedback, and propose a graph recurrent neural network (GRNN) approach to leverage local collaboration among the UAVs. The GRNN method is shown to be robust to variations in UAV speeds and group heading, and scales with the UAV group size. We compare to codebook and binary feedback methods and show that better performance is achieved with the proposed GRNN method.
Linear dimensionality reduction of signals observed by a sensorarray is often useful in balancing the accuracy and speed of post-stage processing, especially in real-time systems with limited computational resources....
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ISBN:
(纸本)9781665405409
Linear dimensionality reduction of signals observed by a sensorarray is often useful in balancing the accuracy and speed of post-stage processing, especially in real-time systems with limited computational resources. However, for multichannel time-series signals having time-invariant intertemporal and interchannel correlations, the direct application of frequency-wise linear dimensionality reduction method requires a large number of digital filters with large filter lengths, which is still unpreferable in the viewpoint of computational cost. We propose a frequency-independent, i.e., instantaneous, linear dimensionality reduction method that achieves low computational cost and latency and high restoration accuracy. We also show several results of numerical experiments to compare the proposed method with other instantaneous linear dimensionality reduction methods, i.e., the principal component analysis and element selection method, and demonstrate the effectiveness of the proposed method.
In this paper, we explore the use of large-scale sparse arrays for pilot placement in pilot-based sensing within integrated sensing and communication (ISAC) systems. Unlike conventional regular pilot placement method,...
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ISBN:
(数字)9798350344813
ISBN:
(纸本)9798350344820
In this paper, we explore the use of large-scale sparse arrays for pilot placement in pilot-based sensing within integrated sensing and communication (ISAC) systems. Unlike conventional regular pilot placement method, sparse placement offers a significant reduction in overhead while maintaining high sensing performance. We present a novel large-scale sparse array construction method by introducing a multi-tier array structure. Using the proposed method, the design of large-scale sparse arrays can be simplified into several smaller-sized array design problems, significantly reducing computational complexity and storage requirements. Numerical examples demonstrate the effectiveness of the proposed method for low-overhead sensing pilot placement design, which is applicable in future 6G applications.
Linear sparse arrays with $N$ physical sensors can resolve $\mathcal{O}(N^{2})$ directions of arrival (DOAs) for uncorrelated sources. This attribute is associated with the difference coarray of size $\mathcal{O}...
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ISBN:
(数字)9798350344813
ISBN:
(纸本)9798350344820
Linear sparse arrays with
$N$
physical sensors can resolve
$\mathcal{O}(N^sensor)$
directions of arrival (DOAs) for uncorrelated sources. This attribute is associated with the difference coarray of size
$\mathcal{O}(N^sensor)$
, However, the number of sources is assumed to be known in many coarray-based DOA estimators. This paper proposes a Monte Carlo source enumerator (MCSE) for sparse arrays by maximizing the log-likelihood function (LLF). This LLF depends on an unbounded parameter space and a multiple integral, which are challenging for computation. This unbounded parameter space is replaced with a bounded space derived from coarse estimates and Cramér-Rao bounds. Next, the multiple integral is approximated with Monte Carlo methods. The MCSE applies to three scenarios: no sources, fewer sources than sensors, and more sources than sensors. Furthermore, some details in the MCSE can be computed in parallel. Numerical examples demonstrate the applicability of the MCSE to sparse arrays.
Factor analysis is widely utilized for data dimensionality reduction. Its objective is to extract a low-rank plus sparse factor structure from the covariance matrix of the observed data. In this paper, we propose a no...
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ISBN:
(数字)9798350344813
ISBN:
(纸本)9798350344820
Factor analysis is widely utilized for data dimensionality reduction. Its objective is to extract a low-rank plus sparse factor structure from the covariance matrix of the observed data. In this paper, we propose a nonconvex and nonsmooth optimization model to obtain the additive matrix decomposition based on the nuclear norm of the low-rank component, the
$\ell_{0}$
norm of the sparse component, and the Kullback-Leibler divergence between the candidate covariance matrix and the sample covariance matrix. We present an ADMM algorithm to solve the optimization problem and demonstrate its effectiveness through experiments on both synthetic and real datasets.
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