Element excitation optimization for phased array fault diagnosis
TL;DR: This work presents techniques for solving the problem of detecting element failures in phased array antennas by using a combination of a single fixed probe and an optimization of element excitations using principles derived from compressive sensing, which results in a reduction in the number of measurements required.
Abstract: We present techniques for solving the problem of detecting element failures in phased array antennas by using a combination of a single fixed probe and an optimization of element excitations using ...
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TL;DR: In this paper , the authors proposed to optimize the antenna excitations to minimize the mutual coherence of the system measurement matrix, leading to a reduced number of measurements required for fault diagnosis.
Abstract: Antenna fault diagnosis for phased antenna arrays is an important research area since faulty elements deteriorate the expected field pattern, leading to degraded performance in various applications. While several compressive sensing-based techniques have been proposed, they rely on a simplified array factor formula, ignoring mutual coupling effects among antennas. We show that this assumption can lead to poor diagnosis in the presence of significant mutual coupling by using two popular models—the average embedded element pattern and a port-level coupling matrix approach. Also, we optimize the antenna excitations to minimize the mutual coherence of the system measurement matrix, leading to a reduced number of measurements required for fault diagnosis. Our simulation results indicate that accounting for the effect of mutual coupling results in a far more reliable diagnosis. In addition, our framework is executed using a single measurement probe fixed in space, thus making a step toward practical fault diagnosis techniques that can be deployed on antenna array systems.
1 citations
22 Mar 2021
TL;DR: In this paper, a fault diagnosis method of a linear antenna array is presented, where the measurements are taken at a fixed location, and excitations are optimized to reduce the number of measurements required for successful diagnosis.
Abstract: Detecting element failure in a phased array antenna plays a crucial role in ensuring a communication system’s efficiency since faulty elements lead to a degradation of the array performance. Under the assumption that only a few antenna elements are faulty, fault diagnosis can be accomplished by applying compressive sensing techniques to solve the resulting system of equations. We present a fault diagnosis method of a linear antenna array, where the measurements are taken at a fixed location, and excitations are optimized. We solve the compressive sensing problem that leads to a reduction in the number of measurements required for successful diagnosis using the optimized excitations. We show how the excitations can be optimized for fault detection in the presence of cosine squared field pattern of an antenna element in a linear array.
1 citations
12 Dec 2022
TL;DR: In this article , a novel idea based on compressive sensing to detect the faulty elements from fixed probe measurements is presented, which is based on the optimizing of the element excitations.
Abstract: The inverse problem for locating faulty elements in an antenna array from electromagnetic field measurements is an interesting and relevant research topic. We present a novel idea based on compressive sensing to detect the faulty elements from fixed probe measurements, which is based on the optimizing of the element excitations. Unlike many previous works, our approach considers the inter-element mutual coupling effects and identifies their importance in fault diagnosis. This study illustrates the effects of coupling in a loop antenna array with varying array sizes. We used two coupling models, namely, the average embedded pattern and the coupling matrix approach to include coupling effects in the analysis. Our work significantly improves antenna array fault diagnosis using the coupling-aware method compared to the existing methods.
12 Dec 2022
TL;DR: In this article , a novel idea based on compressive sensing to detect the faulty elements from fixed probe measurements is presented, which is based on the optimizing of the element excitations.
Abstract: The inverse problem for locating faulty elements in an antenna array from electromagnetic field measurements is an interesting and relevant research topic. We present a novel idea based on compressive sensing to detect the faulty elements from fixed probe measurements, which is based on the optimizing of the element excitations. Unlike many previous works, our approach considers the inter-element mutual coupling effects and identifies their importance in fault diagnosis. This study illustrates the effects of coupling in a loop antenna array with varying array sizes. We used two coupling models, namely, the average embedded pattern and the coupling matrix approach to include coupling effects in the analysis. Our work significantly improves antenna array fault diagnosis using the coupling-aware method compared to the existing methods.
TL;DR: In this paper , the authors proposed to optimize the antenna excitations to minimize the mutual coherence of the system measurement matrix, leading to a reduced number of measurements required for fault diagnosis.
Abstract: Antenna fault diagnosis for phased antenna arrays is an important research area since faulty elements deteriorate the expected field pattern, leading to degraded performance in various applications. While several compressive sensing-based techniques have been proposed, they rely on a simplified array factor formula, ignoring mutual coupling effects among antennas. We show that this assumption can lead to poor diagnosis in the presence of significant mutual coupling by using two popular models—the average embedded element pattern and a port-level coupling matrix approach. Also, we optimize the antenna excitations to minimize the mutual coherence of the system measurement matrix, leading to a reduced number of measurements required for fault diagnosis. Our simulation results indicate that accounting for the effect of mutual coupling results in a far more reliable diagnosis. In addition, our framework is executed using a single measurement probe fixed in space, thus making a step toward practical fault diagnosis techniques that can be deployed on antenna array systems.
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TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
Abstract: Suppose x is an unknown vector in Ropfm (a digital image or signal); we plan to measure n general linear functionals of x and then reconstruct. If x is known to be compressible by transform coding with a known transform, and we reconstruct via the nonlinear procedure defined here, the number of measurements n can be dramatically smaller than the size m. Thus, certain natural classes of images with m pixels need only n=O(m1/4log5/2(m)) nonadaptive nonpixel samples for faithful recovery, as opposed to the usual m pixel samples. More specifically, suppose x has a sparse representation in some orthonormal basis (e.g., wavelet, Fourier) or tight frame (e.g., curvelet, Gabor)-so the coefficients belong to an lscrp ball for 0
18,609 citations
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23 May 2011TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.
Abstract: Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be able to solve problems with a very large number of features or training examples. As a result, both the decentralized collection or storage of these datasets as well as accompanying distributed solution methods are either necessary or at least highly desirable. In this review, we argue that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas. The method was developed in the 1970s, with roots in the 1950s, and is equivalent or closely related to many other algorithms, such as dual decomposition, the method of multipliers, Douglas–Rachford splitting, Spingarn's method of partial inverses, Dykstra's alternating projections, Bregman iterative algorithms for l1 problems, proximal methods, and others. After briefly surveying the theory and history of the algorithm, we discuss applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others. We also discuss general distributed optimization, extensions to the nonconvex setting, and efficient implementation, including some details on distributed MPI and Hadoop MapReduce implementations.
17,433 citations
TL;DR: A new iterative recovery algorithm called CoSaMP is described that delivers the same guarantees as the best optimization-based approaches and offers rigorous bounds on computational cost and storage.
3,970 citations
TL;DR: Candes et al. as discussed by the authors established new results about the accuracy of the reconstruction from undersampled measurements, which improved on earlier estimates, and have the advantage of being more elegant. But they did not consider the restricted isometry property of the sensing matrix.
3,421 citations
TL;DR: It is shown that ‘1 minimization recovers x 0 exactly when the number of measurements exceeds m Const ·µ 2 (U) ·S · logn, where S is the numberof nonzero components in x 0, and µ is the largest entry in U properly normalized: µ(U) = p n · maxk,j |Uk,j|.
Abstract: We consider the problem of reconstructing a sparse signal x 0 2 R n from a limited number of linear measurements. Given m randomly selected samples of Ux 0 , where U is an orthonormal matrix, we show that ‘1 minimization recovers x 0 exactly when the number of measurements exceeds m Const ·µ 2 (U) ·S · logn, where S is the number of nonzero components in x 0 , and µ is the largest entry in U properly normalized: µ(U) = p n · maxk,j |Uk,j|. The smaller µ, the fewer samples needed. The result holds for “most” sparse signals x 0 supported on a fixed (but arbitrary) set T. Given T, if the sign of x 0 for each nonzero entry on T and the observed values of Ux 0 are drawn at random, the signal is recovered with overwhelming probability. Moreover, there is a sense in which this is nearly optimal since any method succeeding with the same probability would require just about this many samples.
2,187 citations