Sparse subspace clustering (SSC) achieves evidenced success in many areas but most related works focus on the centralized scenario. In many practical applications such as security surveillance, several distributed age...
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ISBN:
(数字)9798350344813
ISBN:
(纸本)9798350344820
Sparse subspace clustering (SSC) achieves evidenced success in many areas but most related works focus on the centralized scenario. In many practical applications such as security surveillance, several distributed agents jointly collect a large dataset, while each one is however just able to observe a subset through which a local inference is to be made. This paper proposes a collaborative distributed SSC scheme targeted for this situation. In the proposed scheme, each agent first conducts a fast local data clustering, and transmits basis matrices of the estimated subspaces to the data center, which then conducts subspace information fusion using a k-means type method; the aggregated subspace information is fed back to local agents to update their data partitions. Computer simulations using real human face data are used to illustrate the effectiveness of the proposed method.
Automotive radar emerges as a crucial sensor for autonomous vehicle perception. As more cars are equipped radars, radar interference is an unavoidable challenge. Unlike conventional approaches such as interference mit...
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ISBN:
(数字)9798350344813
ISBN:
(纸本)9798350344820
Automotive radar emerges as a crucial sensor for autonomous vehicle perception. As more cars are equipped radars, radar interference is an unavoidable challenge. Unlike conventional approaches such as interference mitigation and interference avoiding technologies, this paper introduces an innovative collaborative sensing scheme with multiple automotive radars that exploits constructive interference. Through collaborative sensing, our method optimally aligns cross-path interference signals from other radars with another radar's self-echo signals, thereby significantly augmenting its target detection capabilities. This approach alleviates the need for extensive raw data sharing between collaborating radars. Instead, only an optimized weighting matrix needs to be exchanged between the radars. This approach considerably decreases the data bandwidth requirements for the wireless channel, making it a more feasible and practical solution for automotive radar collaboration. Numerical results demonstrate the effectiveness of the constructive interference approach for enhanced object detection capability.
Covariance matrix estimation is a crucial problem in many areas related to data analysis. While centralized sparse covariance matrix estimators have received extensive attention, practical considerations such as commu...
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ISBN:
(数字)9798350344813
ISBN:
(纸本)9798350344820
Covariance matrix estimation is a crucial problem in many areas related to data analysis. While centralized sparse covariance matrix estimators have received extensive attention, practical considerations such as communication efficiency and privacy constraints often make centralizing data impractical in many real-world scenarios. This necessitates the development of distributed covariance matrix estimation methods. In this paper, we present a novel distributed estimator for a sparse covariance matrix over networks by minimizing the sum of all agents' losses based on
$\ell_{1}$
penalized Gaussian likelihood. To solve this constrained non-convex, non-Lipschitz-smooth optimization problem without relying on a central processor, we propose a straightforward network covariance iterative shrinkage-thresholding algorithm (network C-ISTA) with provable convergence. Numerical simulations demonstrate the convergence and impressive estimation performance of the network C-ISTA algorithm, confirming its effectiveness under decentralized settings.
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 ...
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ISBN:
(数字)9798350344813
ISBN:
(纸本)9798350344820
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 APV s 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.
In this paper, we investigate the transmit signal de-sign problem for a dual-functional radar-communication (DFRC) system equipped with one-bit digital-to-analog converters (DACs). Specifically, the one-bit DFRC wavef...
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ISBN:
(数字)9798350344813
ISBN:
(纸本)9798350344820
In this paper, we investigate the transmit signal de-sign problem for a dual-functional radar-communication (DFRC) system equipped with one-bit digital-to-analog converters (DACs). Specifically, the one-bit DFRC waveform is designed to minimize the difference between the transmitted beampattern and a desired one, while ensuring constructive interference (CI)-based QoS constraints for communication users. The formulated problem is a discrete optimization problem with a nonconvex objective function and many linear constraints. To solve it, we first propose a penalty model to transform the discrete problem into a continuous one. Then, we propose an inexact augmented Lagrangian method (ALM) framework to solve the penalty model. In particular, the ALM subproblem at each iteration is solved by a custom-designed block successive upper-bound minimization (BSUM) algorithm, which admits closed-form updates and thus makes the proposed approach computationally efficient. Simulation results verify the superiority of the proposed approach over the existing ones in both the radar and communication performance.
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:
(数字)9798350344813
ISBN:
(纸本)9798350344820
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.
Modulo sampling and dithered one-bit quantization frame-works have emerged as promising solutions to overcome the limitations of traditional analog-to-digital converters (ADCs) and sensors. Modulo sampling, with its h...
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ISBN:
(数字)9798350344813
ISBN:
(纸本)9798350344820
Modulo sampling and dithered one-bit quantization frame-works have emerged as promising solutions to overcome the limitations of traditional analog-to-digital converters (ADCs) and sensors. Modulo sampling, with its high-resolution approach utilizing modulo ADCs, offers an unlimited dynamic range, while dithered one-bit quantization offers cost-efficiency and reduced power consumption while operating at elevated sampling rates. Our goal is to explore the synergies between these two techniques, leveraging their unique advantages, and to apply them to non-bandlimited signals within spline spaces. One noteworthy application of these signals lies in High Dynamic Range (HDR) imaging. In this paper, we expand upon the Unlimited One-Bit (UNO) sampling framework, initially conceived for bandlimited signals as proposed in [1], to encompass non-bandlimited signals found in the context of HDR imaging. We present a novel algorithm rigorously examined for its ability to recover images from one-bit modulo samples. Additionally, we introduce a sufficient condition specifically designed for UNO sampling to properly recover non-bandlimited signals within spline spaces. Our numerical results vividly demonstrate the effectiveness of UNO sampling in the realm of HDR imaging.
In this paper, we consider the downlink channel estimation for the intelligent reflecting surface (IRS) assisted MIMO systems in the millimeter wave band. It is challenging to perform the channel estimation for the IR...
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ISBN:
(数字)9798350344813
ISBN:
(纸本)9798350344820
In this paper, we consider the downlink channel estimation for the intelligent reflecting surface (IRS) assisted MIMO systems in the millimeter wave band. It is challenging to perform the channel estimation for the IRS-assisted wireless communication systems, due to the large amount of passive reflecting elements at the IRS. On account of the inherent sparsity of millimeter-wave channels, the sparse representation expressions of the channel between the base station (BS) and IRS and that between the IRS and user equipment (UE) are obtained separately. Furthermore, the cascaded channel is estimated by solving the BS-IRS and IRS-UE channels in an alternating way. To improve the estimation accuracy and reduce the pilot over-head, the algorithm of the block-structured orthogonal matching pursuit (BS-OMP) is devised to estimate the BS-IRS channel by exploiting its block sparsity. The simulation results show that the proposed method provides the higher estimation accuracy and consumes less pilot overhead by comparing with its counterparts.
This paper addresses the problem of spatial waveform design for collocated multiple-input multiple-output (MIMO) radar systems with sparse antenna arrays. The use of sparse arrays allows to obtain narrower beams and t...
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ISBN:
(数字)9798350344813
ISBN:
(纸本)9798350344820
This paper addresses the problem of spatial waveform design for collocated multiple-input multiple-output (MIMO) radar systems with sparse antenna arrays. The use of sparse arrays allows to obtain narrower beams and therefore higher angular resolution and accuracy. However, if the spatial waveform is not designed properly, the resulting transmit-receive beam-pattern may suffer from significant sidelobes or ambiguity, which can strongly degrade the estimation performance. The Bayesian Cramer-Rao bound (BCRB), which is commonly used for waveform design, may produce inappropriate results as it considers only local errors and ignores the effect of sidelobes and ambiguity. To overcome this limitation, we propose using the arbitrary test-point transformation Weiss-Weinstein bound (AT-WWB) that was recently proposed, as an optimization criterion. This bound is a simpler and tighter version of the Weiss-Weinstein bound (WWB). This bound is derived for collocated MIMO radar and is minimized with respect to the ratio between coherent and orthogonal signals. The proposed method is demonstrated via simulations, and compared to optimization schemes using the BCRB and the WWB. It is shown that the spatial waveform optimized by AT- WWB exhibits superior performance in terms of direction-of-arrival estimation accuracy.
We develop a novel continuous optimization algorithm to recover latent directed acyclic graphs (DAGs) from observational (and possibly heteroscedastic) data adhering to a linear structural equation model (SEM). Our st...
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ISBN:
(数字)9798350344813
ISBN:
(纸本)9798350344820
We develop a novel continuous optimization algorithm to recover latent directed acyclic graphs (DAGs) from observational (and possibly heteroscedastic) data adhering to a linear structural equation model (SEM). Our starting point is the recently proposed Concomitant Linear DAG Estimation (CoLiDE) framework, which advocates minimizing a sparsity-regularized convex score function augmented with a smooth, nonconvex acyclicity penalty. While prior work focused on score function design to jointly estimate DAG structure along with exogenous noise levels, optimization aspects were left unexplored. To bridge this gap, here we show that CoLiDE has a favorable structure amenable to optimization via a block successive convex approximation (BSCA) algorithm. We derive efficient, closed-form updates to refine the DAG adjacency matrix and noise variance estimates in a cyclic fashion. Although the acyclicity regularizer is devoid of a Lipschitz gradient and hence our approximation function is not a global upper bound of the original cost, a descent direction can be obtained via line search to yield a provably convergent sequence. Numerical tests showcase the superiority of the proposed BSCA iterations relative to the original (Adam-based) inexact block coordinate descent solver.
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