Pub Date : 2024-09-10DOI: 10.1109/TSP.2024.3457159
Congwei Feng;Huawei Chen
Beampattern synthesis inspired by adaptive array theory (AAT) has attracted much interest in recent years, thanks to its capability to flexibly and precisely control beampattern. However, the existing AAT-inspired beampattern synthesis approaches usually assume an ideal array model, which is not realistic in practice and may lead to severe performance degradation in the presence of steering vector errors. In this paper, we propose a robust beampattern synthesis approach for wideband arrays using regularized AAT-inspired weighted least squares (WLS), which can precisely control the worst-case beampattern, including both its mainlobe ripple and sidelobe level, in the presence of steering vector errors. We develop a theory on the solutions for the regularization parameter and weighting function of the regularized AAT-inspired WLS. We propose a Newton-Raphson method to find the solution for the regularization parameter, and derive closed-form solutions for the weighting function. Moreover, we also offer some insight into the effect of steering vector errors on the control of worst-case beampattern. The effectiveness of the proposed algorithm is verified by design examples, including robust synthesis of frequency-invariant and flat-top wideband beampatterns.
{"title":"Robust Wideband Beampattern Synthesis With Precise Control of Worst-Case Beampattern","authors":"Congwei Feng;Huawei Chen","doi":"10.1109/TSP.2024.3457159","DOIUrl":"10.1109/TSP.2024.3457159","url":null,"abstract":"Beampattern synthesis inspired by adaptive array theory (AAT) has attracted much interest in recent years, thanks to its capability to flexibly and precisely control beampattern. However, the existing AAT-inspired beampattern synthesis approaches usually assume an ideal array model, which is not realistic in practice and may lead to severe performance degradation in the presence of steering vector errors. In this paper, we propose a robust beampattern synthesis approach for wideband arrays using regularized AAT-inspired weighted least squares (WLS), which can precisely control the worst-case beampattern, including both its mainlobe ripple and sidelobe level, in the presence of steering vector errors. We develop a theory on the solutions for the regularization parameter and weighting function of the regularized AAT-inspired WLS. We propose a Newton-Raphson method to find the solution for the regularization parameter, and derive closed-form solutions for the weighting function. Moreover, we also offer some insight into the effect of steering vector errors on the control of worst-case beampattern. The effectiveness of the proposed algorithm is verified by design examples, including robust synthesis of frequency-invariant and flat-top wideband beampatterns.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4573-4588"},"PeriodicalIF":4.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142166021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Principal Component Analysis (PCA) is a workhorse of modern data science. While PCA assumes the data conforms to Euclidean geometry, for specific data types, such as hierarchical and cyclic data structures, other spaces are more appropriate. We study PCA in space forms; that is, those with constant curvatures. At a point on a Riemannian manifold, we can define a Riemannian affine subspace based on a set of tangent vectors. Finding the optimal low-dimensional affine subspace for given points in a space form amounts to dimensionality reduction. Our Space Form PCA (SFPCA) seeks the affine subspace that best represents a set of manifold-valued points with the minimum projection cost. We propose proper cost functions that enjoy two properties: (1) their optimal affine subspace is the solution to an eigenequation, and (2) optimal affine subspaces of different dimensions form a nested set. These properties provide advances over existing methods, which are mostly iterative algorithms with slow convergence and weaker theoretical guarantees. We evaluate the proposed SFPCA on real and simulated data in spherical and hyperbolic spaces. We show that it outperforms alternative methods in estimating true subspaces (in simulated data) with respect to convergence speed or accuracy, often both.
主成分分析（PCA）是现代数据科学的主要工具。虽然 PCA 假设数据符合欧几里得几何学，但对于特定的数据类型，如分层和循环数据结构，其他空间更为合适。我们研究的是空间形式的 PCA，即具有恒定曲率的空间形式。在黎曼流形上的某一点，我们可以根据一组切向量定义一个黎曼仿射子空间。为空间形式中的给定点找到最佳低维仿射子空间相当于降维。我们的空间形式 PCA（Space Form PCA，SFPCA）寻求的是以最小投影成本最好地代表一组流形值点的仿射子空间。我们提出的适当成本函数具有两个特性：（1）其最优仿射子空间是一个特征方程的解，（2）不同维度的最优仿射子空间形成一个嵌套集。这些特性是现有方法的进步所在，现有方法大多是迭代算法，收敛速度慢，理论保证较弱。我们在球面空间和双曲空间的真实数据和模拟数据上对所提出的 SFPCA 进行了评估。结果表明，在估计真实子空间（模拟数据）方面，SFPCA 在收敛速度或准确性（通常两者兼而有之）方面优于其他方法。
{"title":"Principal Component Analysis in Space Forms","authors":"Puoya Tabaghi;Michael Khanzadeh;Yusu Wang;Siavash Mirarab","doi":"10.1109/TSP.2024.3457529","DOIUrl":"10.1109/TSP.2024.3457529","url":null,"abstract":"Principal Component Analysis (PCA) is a workhorse of modern data science. While PCA assumes the data conforms to Euclidean geometry, for specific data types, such as hierarchical and cyclic data structures, other spaces are more appropriate. We study PCA in space forms; that is, those with constant curvatures. At a point on a Riemannian manifold, we can define a Riemannian affine subspace based on a set of tangent vectors. Finding the optimal low-dimensional affine subspace for given points in a space form amounts to dimensionality reduction. Our Space Form PCA (SFPCA) seeks the affine subspace that best represents a set of manifold-valued points with the minimum projection cost. We propose proper cost functions that enjoy two properties: (1) their optimal affine subspace is the solution to an eigenequation, and (2) optimal affine subspaces of different dimensions form a nested set. These properties provide advances over existing methods, which are mostly iterative algorithms with slow convergence and weaker theoretical guarantees. We evaluate the proposed SFPCA on real and simulated data in spherical and hyperbolic spaces. We show that it outperforms alternative methods in estimating true subspaces (in simulated data) with respect to convergence speed or accuracy, often both.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4428-4443"},"PeriodicalIF":4.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670424","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142166477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-05DOI: 10.1109/tsp.2024.3454972
Linfeng Xu, X. Rong Li, Mahendra Mallick, Zhansheng Duan
{"title":"Modeling and State Estimation of Destination-Constrained Dynamic Systems. Part II: Uncertain Arrival Time","authors":"Linfeng Xu, X. Rong Li, Mahendra Mallick, Zhansheng Duan","doi":"10.1109/tsp.2024.3454972","DOIUrl":"https://doi.org/10.1109/tsp.2024.3454972","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"42 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142142665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-05DOI: 10.1109/TSP.2024.3454703
Ayano Nakai-Kasai;Tadashi Wadayama
The required signal processing rate in future wireless communication systems exceeds the performance of the latest electronics-based processors. Introduction of analog optical computation is one promising direction for energy-efficient processing. This paper considers a continuous-time minimum mean squared error detection for multiple-input multiple-output systems to realize signal detection using analog optical devices. The proposed method is formulated by an ordinary differential equation (ODE) and its performance at any continuous time can be theoretically analyzed. Deriving and analyzing the continuous-time system is a meaningful step to verifying the feasibility of analog-domain signal processing in the future systems. In addition, considering such an ODE brings byproducts to discrete-time detection algorithms, which can be a novel methodology of algorithm construction and analysis.
{"title":"Ordinary Differential Equation-Based MIMO Signal Detection","authors":"Ayano Nakai-Kasai;Tadashi Wadayama","doi":"10.1109/TSP.2024.3454703","DOIUrl":"10.1109/TSP.2024.3454703","url":null,"abstract":"The required signal processing rate in future wireless communication systems exceeds the performance of the latest electronics-based processors. Introduction of analog optical computation is one promising direction for energy-efficient processing. This paper considers a continuous-time minimum mean squared error detection for multiple-input multiple-output systems to realize signal detection using analog optical devices. The proposed method is formulated by an ordinary differential equation (ODE) and its performance at any continuous time can be theoretically analyzed. Deriving and analyzing the continuous-time system is a meaningful step to verifying the feasibility of analog-domain signal processing in the future systems. In addition, considering such an ODE brings byproducts to discrete-time detection algorithms, which can be a novel methodology of algorithm construction and analysis.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4147-4162"},"PeriodicalIF":4.6,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142142762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, tensor singular value decomposition (t-SVD), based on the tensor-tensor product (t-product), has become a powerful tool for processing third-order tensor data. However, constrained by the fact that the basic element is the fiber (i.e., vector) in the t-product, higher-order tensor data (i.e., order $d>3$ ) are usually unfolded into third-order tensors to satisfy the classical t-product setting, which leads to the destroying of high-dimensional structure. By revisiting the basic element in the t-product, we suggest a generalized t-product called element-based tensor-tensor product (elt-product) as an alternative of the classic t-product, where the basic element is a $(d-2)$ th-order tensor instead of a vector. The benefit of the elt-product is that it can better preserve high-dimensional structures and that it can explore more complex interactions via higher-order convolution instead of first-order convolution in classic t-product. Starting from the elt-product, we develop new tensor-SVD and low-rank tensor metrics (e.g., rank and nuclear norm). Equipped with the suggested metrics, we present a tensor completion model for high-order tensor data and prove the exact recovery guarantees. To harness the resulting nonconvex optimization problem, we apply an alternating direction method of the multiplier (ADMM) algorithm with a theoretical convergence guarantee. Extensive experimental results on the simulated and real-world data (color videos, light-field images, light-field videos, and traffic data) demonstrate the superiority of the proposed model against the state-of-the-art baseline models.
{"title":"Revisiting High-Order Tensor Singular Value Decomposition From Basic Element Perspective","authors":"Sheng Liu;Xi-Le Zhao;Jinsong Leng;Ben-Zheng Li;Jing-Hua Yang;Xinyu Chen","doi":"10.1109/TSP.2024.3454115","DOIUrl":"10.1109/TSP.2024.3454115","url":null,"abstract":"Recently, tensor singular value decomposition (t-SVD), based on the tensor-tensor product (t-product), has become a powerful tool for processing third-order tensor data. However, constrained by the fact that the basic element is the fiber (i.e., vector) in the t-product, higher-order tensor data (i.e., order \u0000<inline-formula><tex-math>$d>3$</tex-math></inline-formula>\u0000) are usually unfolded into third-order tensors to satisfy the classical t-product setting, which leads to the destroying of high-dimensional structure. By revisiting the basic element in the t-product, we suggest a generalized t-product called element-based tensor-tensor product (elt-product) as an alternative of the classic t-product, where the basic element is a \u0000<inline-formula><tex-math>$(d-2)$</tex-math></inline-formula>\u0000th-order tensor instead of a vector. The benefit of the elt-product is that it can better preserve high-dimensional structures and that it can explore more complex interactions via higher-order convolution instead of first-order convolution in classic t-product. Starting from the elt-product, we develop new tensor-SVD and low-rank tensor metrics (e.g., rank and nuclear norm). Equipped with the suggested metrics, we present a tensor completion model for high-order tensor data and prove the exact recovery guarantees. To harness the resulting nonconvex optimization problem, we apply an alternating direction method of the multiplier (ADMM) algorithm with a theoretical convergence guarantee. Extensive experimental results on the simulated and real-world data (color videos, light-field images, light-field videos, and traffic data) demonstrate the superiority of the proposed model against the state-of-the-art baseline models.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4589-4603"},"PeriodicalIF":4.6,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The multivariate generalized Gaussian distribution (MGGD), also known as the multivariate exponential power (MEP) distribution, is widely used in signal and image processing. However, estimating MGGD parameters, which is required in practical applications, still faces specific theoretical challenges. In particular, establishing convergence properties for the standard fixed-point approach when both the distribution mean and the scatter (or the precision) matrix are unknown is still an open problem. In robust estimation, imposing classical constraints on the precision matrix, such as sparsity, has been limited by the non-convexity of the resulting cost function. This paper tackles these issues from an optimization viewpoint by proposing a convex formulation with well-established convergence properties. We embed our analysis in a noisy scenario where robustness is induced by modelling multiplicative perturbations. The resulting framework is flexible as it combines a variety of regularizations for the precision matrix, the mean and model perturbations. This paper presents proof of the desired theoretical properties, specifies the conditions preserving these properties for different regularization choices and designs a general proximal primal-dual optimization strategy. The experiments show a more accurate precision and covariance matrix estimation with similar performance for the mean vector parameter compared to Tyler's $M$ -estimator. In a high-dimensional setting, the proposed method outperforms the classical GLASSO, one of its robust extensions, and the regularized Tyler's estimator.
{"title":"Convex Parameter Estimation of Perturbed Multivariate Generalized Gaussian Distributions","authors":"Nora Ouzir;Frédéric Pascal;Jean-Christophe Pesquet","doi":"10.1109/TSP.2024.3453509","DOIUrl":"10.1109/TSP.2024.3453509","url":null,"abstract":"The multivariate generalized Gaussian distribution (MGGD), also known as the multivariate exponential power (MEP) distribution, is widely used in signal and image processing. However, estimating MGGD parameters, which is required in practical applications, still faces specific theoretical challenges. In particular, establishing convergence properties for the standard fixed-point approach when both the distribution mean and the scatter (or the precision) matrix are unknown is still an open problem. In robust estimation, imposing classical constraints on the precision matrix, such as sparsity, has been limited by the non-convexity of the resulting cost function. This paper tackles these issues from an optimization viewpoint by proposing a convex formulation with well-established convergence properties. We embed our analysis in a noisy scenario where robustness is induced by modelling multiplicative perturbations. The resulting framework is flexible as it combines a variety of regularizations for the precision matrix, the mean and model perturbations. This paper presents proof of the desired theoretical properties, specifies the conditions preserving these properties for different regularization choices and designs a general proximal primal-dual optimization strategy. The experiments show a more accurate precision and covariance matrix estimation with similar performance for the mean vector parameter compared to Tyler's \u0000<inline-formula><tex-math>$M$</tex-math></inline-formula>\u0000-estimator. In a high-dimensional setting, the proposed method outperforms the classical GLASSO, one of its robust extensions, and the regularized Tyler's estimator.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4132-4146"},"PeriodicalIF":4.6,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-03DOI: 10.1109/TSP.2024.3453346
Yuanbo Cheng;Johan Karlsson;Jian Li
To mitigate the dynamic range problems that low-bit quantization of conventional analog-to-digital converters (ADCs) suffer from, we shift our attention to the novel modulo ADCs (Mod-ADCs). We consider the Cramér-Rao bound (CRB) analysis for signal parameter estimation from Mod-ADC generated data. Four CRB formulas are derived assuming known or unknown folding-counts, for both quantized and unquantized cases. We analyze many of their characteristics, such as monotonicity, boundedness and convergence; and perform detailed comparisons of the CRBs among the conventional ADCs and the two different types of Mod-ADCs. Numerical examples are presented to demonstrate these characteristics, and that the low-bit Mod-ADCs can provide satisfactory signal parameter estimation performances even in high dynamic range situations.
{"title":"Cramér-Rao Bound for Signal Parameter Estimation From Modulo ADC Generated Data","authors":"Yuanbo Cheng;Johan Karlsson;Jian Li","doi":"10.1109/TSP.2024.3453346","DOIUrl":"10.1109/TSP.2024.3453346","url":null,"abstract":"To mitigate the dynamic range problems that low-bit quantization of conventional analog-to-digital converters (ADCs) suffer from, we shift our attention to the novel modulo ADCs (Mod-ADCs). We consider the Cramér-Rao bound (CRB) analysis for signal parameter estimation from Mod-ADC generated data. Four CRB formulas are derived assuming known or unknown folding-counts, for both quantized and unquantized cases. We analyze many of their characteristics, such as monotonicity, boundedness and convergence; and perform detailed comparisons of the CRBs among the conventional ADCs and the two different types of Mod-ADCs. Numerical examples are presented to demonstrate these characteristics, and that the low-bit Mod-ADCs can provide satisfactory signal parameter estimation performances even in high dynamic range situations.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4268-4285"},"PeriodicalIF":4.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1109/TSP.2024.3451412
Zengchenghao Xia;Zhiyong Hu;Qingbo He;Chao Wang
Active learning provides guidance for the design and modeling of systems with highly expensive sampling costs. However, existing active learning approaches suffer from cold-start concerns, where the performance is impaired due to the initial few experiments designed by active learning. In this paper, we propose using transfer learning to solve the cold-start problem of functional regression by leveraging knowledge from related and data-rich signals to achieve robust and superior performance, especially when only a few experiments are available in the signal of interest. More specifically, we construct a multi-output Gaussian process (MGP) to model the between-signal functional relationship. This MGP features unique innovations that distinguish the proposed transfer active learning from existing works: i) a specially designed covariance structure is proposed for characterizing within-and between-signal inter-relationships and facilitating interpretable transfer learning, and ii) an iterative Bayesian framework is proposed to update the parameters and prediction of the MGP in real-time, which significantly reduces the computational load and facilitates the iterative active learning. The inter-relationship captured by this novel MGP is then fed into active learning using the integrated mean-squared error (IMSE) as the objective. We provide theoretical justifications for this active learning mechanism, which demonstrates the objective (IMSE) is monotonically decreasing as we gather more data from the proposed transfer active learning. The real-time updating and the monotonically decreasing objective together provide both practical efficiency and theoretical guarantees for solving the cold-start concern in active learning. The proposed method is compared with benchmark methods through various numerical and real case studies, and the results demonstrate the superiority of the method, especially when limited experiments are available at the initial stage of design.
{"title":"Real-Time Transfer Active Learning for Functional Regression and Prediction Based on Multi-Output Gaussian Process","authors":"Zengchenghao Xia;Zhiyong Hu;Qingbo He;Chao Wang","doi":"10.1109/TSP.2024.3451412","DOIUrl":"10.1109/TSP.2024.3451412","url":null,"abstract":"Active learning provides guidance for the design and modeling of systems with highly expensive sampling costs. However, existing active learning approaches suffer from cold-start concerns, where the performance is impaired due to the initial few experiments designed by active learning. In this paper, we propose using transfer learning to solve the cold-start problem of functional regression by leveraging knowledge from related and data-rich signals to achieve robust and superior performance, especially when only a few experiments are available in the signal of interest. More specifically, we construct a multi-output Gaussian process (MGP) to model the between-signal functional relationship. This MGP features unique innovations that distinguish the proposed transfer active learning from existing works: i) a specially designed covariance structure is proposed for characterizing within-and between-signal inter-relationships and facilitating interpretable transfer learning, and ii) an iterative Bayesian framework is proposed to update the parameters and prediction of the MGP in real-time, which significantly reduces the computational load and facilitates the iterative active learning. The inter-relationship captured by this novel MGP is then fed into active learning using the integrated mean-squared error (IMSE) as the objective. We provide theoretical justifications for this active learning mechanism, which demonstrates the objective (IMSE) is monotonically decreasing as we gather more data from the proposed transfer active learning. The real-time updating and the monotonically decreasing objective together provide both practical efficiency and theoretical guarantees for solving the cold-start concern in active learning. The proposed method is compared with benchmark methods through various numerical and real case studies, and the results demonstrate the superiority of the method, especially when limited experiments are available at the initial stage of design.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4163-4177"},"PeriodicalIF":4.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142123822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1109/TSP.2024.3452239
Yibin Liu;Shengbin Luo Wang;Guoqing Wu;Ping Wang;Yongzhen Li
Four-channel monopulse (FCM) technique is integral to contemporary radar systems for pinpointing and tracking multiple targets. However, the practical utility of FCM is significantly hindered by the issue of estimation failure, which arises from the specific relative phases and positions of the targets. In this paper, we introduce a polarimetric four-channel monopulse (PFCM) approach designed to deliver efficient, robust, and unambiguous resolution of two targets. Generally, PFCM adaptively modulates the null-polarization for each target. We begin by dissecting the limitations of the traditional method, utilizing the closed-form solution of FCM. Subsequently, we devise a novel closed-form solution that harnesses polarimetric adaptive nulling (PAN) technology to estimate the angles of the two targets under two distinct scenarios: the ideal condition and angular ambiguity. Under ideal conditions, estimation is conducted directly using the received signals. Incorporating polarization information mitigates the estimation results’ sensitivity to relative phase. To counteract the challenges of PAN failure in angular ambiguity situations, the proposed PFCM adeptly resolves the two targets by reconstructing the four-channel signal, indicating its applicability across various relative target positions. Furthermore, we present a comprehensive performance analysis focusing on three critical aspects. As a result, two closely spaced targets can be resolved robustly and unambiguously with minimal complexity. Numerical simulations and experimental results are substantiated to validate the effectiveness of the proposed method.
四通道单脉冲（FCM）技术是当代雷达系统精确定位和跟踪多个目标不可或缺的技术。然而，由于目标的特定相对相位和位置造成的估计失败问题，极大地阻碍了 FCM 的实际应用。在本文中，我们介绍了一种偏振四通道单脉冲（PFCM）方法，旨在高效、稳健、准确地分辨两个目标。一般来说，PFCM 可自适应地调节每个目标的空极化。我们首先利用 FCM 的闭式解剖析了传统方法的局限性。随后，我们设计了一种新型闭式解决方案，利用偏振自适应归零（PAN）技术在两种不同情况下估计两个目标的角度：理想情况和角度模糊情况。在理想条件下，估算直接使用接收到的信号。加入极化信息可减轻估计结果对相对相位的敏感性。为了应对角度模糊情况下 PAN 失效所带来的挑战，所提出的 PFCM 通过重建四通道信号巧妙地分辨出两个目标，这表明它适用于各种相对目标位置。此外，我们还针对三个关键方面进行了全面的性能分析。其结果是，两个间隔很近的目标能以最低的复杂度被稳健、明确地分辨出来。数值模拟和实验结果证实了所提方法的有效性。
{"title":"Robust and Unambiguous Four-Channel Monopulse Two-Target Resolution: A Polarimetric Closed-Form Approach","authors":"Yibin Liu;Shengbin Luo Wang;Guoqing Wu;Ping Wang;Yongzhen Li","doi":"10.1109/TSP.2024.3452239","DOIUrl":"10.1109/TSP.2024.3452239","url":null,"abstract":"Four-channel monopulse (FCM) technique is integral to contemporary radar systems for pinpointing and tracking multiple targets. However, the practical utility of FCM is significantly hindered by the issue of estimation failure, which arises from the specific relative phases and positions of the targets. In this paper, we introduce a polarimetric four-channel monopulse (PFCM) approach designed to deliver efficient, robust, and unambiguous resolution of two targets. Generally, PFCM adaptively modulates the null-polarization for each target. We begin by dissecting the limitations of the traditional method, utilizing the closed-form solution of FCM. Subsequently, we devise a novel closed-form solution that harnesses polarimetric adaptive nulling (PAN) technology to estimate the angles of the two targets under two distinct scenarios: the ideal condition and angular ambiguity. Under ideal conditions, estimation is conducted directly using the received signals. Incorporating polarization information mitigates the estimation results’ sensitivity to relative phase. To counteract the challenges of PAN failure in angular ambiguity situations, the proposed PFCM adeptly resolves the two targets by reconstructing the four-channel signal, indicating its applicability across various relative target positions. Furthermore, we present a comprehensive performance analysis focusing on three critical aspects. As a result, two closely spaced targets can be resolved robustly and unambiguously with minimal complexity. Numerical simulations and experimental results are substantiated to validate the effectiveness of the proposed method.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4222-4236"},"PeriodicalIF":4.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142123823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-30DOI: 10.1109/TSP.2024.3452035
Feng Zhu;Jingjing Zhang;Xin Wang
Synchronous local stochastic gradient descent (local SGD) suffers from some workers being idle and random delays due to slow and straggling workers, as it waits for the workers to complete the same amount of local updates. To address this issue, a novel local SGD strategy called STSyn is proposed in this paper. The key point is to wait for the $K$ fastest workers while keeping all the workers computing continually at each synchronization round, and making full use of any effective (completed) local update of each worker regardless of stragglers. To evaluate the performance of STSyn, an analysis of the average wall-clock time, average number of local updates, and average number of uploading workers per round is provided. The convergence of STSyn is also rigorously established even when the objective function is nonconvex for both homogeneous and heterogeneous data distributions. Experimental results highlight the superiority of STSyn over state-of-the-art schemes, thanks to its straggler-tolerant technique and the inclusion of additional effective local updates at each worker. Furthermore, the impact of system parameters is investigated. By waiting for faster workers and allowing heterogeneous synchronization with different numbers of local updates across workers, STSyn provides substantial improvements both in time and communication efficiency.
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