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Synthesis of Large sparse sensor arrays Utilizing Relaxed-Intensified Exploration Algorithm (RIEA) for Optimal UAVs Beamforming

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Zhou, Zhigang Zeng, Cao Lan, Lan Liao, Guisheng Zhu, Shengqi Chen, Baixiao Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

In this study, we present a novel relaxed-intensified exploration algorithm (RIEA) to synthesize large-aperture sensor arrays producing good array sparsity and optimal weight vector of the sparse sensor arrays for sensing unmanned aerial vehicles (UAVs) in airspace. The proposed algorithm is based on the compressed-sensing framework integrated with a kind of relaxed-intensified optimization thought, which comprises two core stages: the relaxed optimization stage and the intensified reoptimization stage. The relaxed-intensified exploration algorithm (RIEA) is tailored to accelerate array synthesis efficiency and promote global optimization. For the proposed algorithm, the ability to approach the global convergence is embodied in two key stages. The first stage aims to generate an optimal sparse sensor array with arbitrary upper mask constraints, whose upper mask is slightly relaxed to expand the solution space for further enhancing the array sparsity. Meanwhile, direction dimension reduction is further conducted to relax the radiating direction matrix for reducing massive computational cost. For the intensified reoptimization stage, the "relaxed" upper mask is first readjusted back to the strictly constrained strength and the weight vector of the designed sparse sensor array in the previous stage is then further optimized to approach the global optimal solution. Finally, the presence of element pattern for an individual sensor and array beam-scanning capability are also considered and investigated in synthesizing the sparse sensor arrays for precise positioning and sensing of UAVs. Several representative examples of the small/large-aperture sparse sensor arrays are performed to demonstrate the superiority, effectiveness, and robustness of the proposed RIEA.

关键词： sensor arrays Phased arrays Optimization Autonomous aerial vehicles Vectors Array signal processing Signal processing algorithms Layout Minimization Computational efficiency Array pattern synthesis dimension reduction lower time cost relaxed-intensified optimization thought sparse sensor arrays

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Potential and Limitations of a Deconvolution Approach for Guided Wave Structural Health Monitoring

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2009年第5期8卷 381-395页

作者： Cicero, T. Cawley, P. Simonetti, F. Rokhlin, S. I. Univ London Imperial Coll Sci Technol & Med UK Res Ctr NDE London SW7 2AZ England Ohio State Univ Columbus OH 43210 USA

Ultrasonic guided waves offer the possibility of inspecting large areas of structures from a small number of sensor positions. However, inspection of complex structures is difficult as the reflections from different features overlap. Estimating the number and amplitude of the wave packets in ultrasonic time traces is crucial for the development of a guided wave inspection system, in order to detect and locate damage. Deconvolution has been extensively used in geophysical applications to resolve overlapping echoes in the recorded signals. The main objective of this work was to evaluate the applicability of the deconvolution approach for enhancing the resolution of ultrasonic time traces in structural health monitoring (SHM). Numerical simulations on strongly overlapping signals were carried out to evaluate the performance of the two techniques that have been considered: (i) Wiener filter, (ii) single most likely replacement. It was shown that the relatively narrow bandwidth of the input signals and phase shifts between different reflections limit the benefits obtained from deconvolution and it was concluded that deconvolution is unlikely to be useful for guided wave SHM applications.

关键词： structural health monitoring sparse sensor arrays signal processing signal deconvolution

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Adaptive Antenna Array Beamforming Based on Norm Penalized NLMS Algorithm

Adaptive Antenna Array Beamforming Based on Norm Penalized N...

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IEEE-Antennas-and-Propagation-Society International Symposium on Antennas and Propagation / USNC/URSI National Radio Science Meeting

作者： Shi, Wanlu Li, Yingsong Luo, Shengyuan Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Heilongjiang Peoples R China Chinese Acad Sci Natl Space Sci Ctr Key Lab Microwave Remote Sensing Beijing 100190 Peoples R China

ISBN: (纸本)9781538671023

An l(0)-norm constrained normalized LMS algorithm (L-0-CNLMS) is proposed to find out the solution of the thinned array adaptive beamforming. The proposed L-0-CNLMS integrates an additional l(0)-norm penalty on the filter coefficients compared with the constrained NLMS (CNLMS) algorithm, which aims to seek the solution towards sparsity and finally thinned array. To reduce the computation burden, an approximating l(0)-norm method is presented and integrated into the CNLMS algorithm. The simulation experiments are created to show that the L-0-CNLMS algorithm is effective in sparse adaptive beamforming.

关键词： l(0)-norm NLMS algorithm sparse sensor arrays constrained adaptive beamforming

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Coherence analysis using canonical coordinate decomposition with applications to sparse processing and optimal array deployment

Coherence analysis using canonical coordinate decomposition ...

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Conference on Unattended/Unmanned Ground, Ocean and Air sensor Technologies and Applications VI

作者： Azimi-Sadjadi, MR Pezeshki, A Wade, R Informat Syst Technol Inc Ft Collins CO USA

ISBN: (纸本)0819453404

sparse array processing methods are typically used to improve the spatial resolution of sensor arrays for the estimation of direction of arrival (DOA). The fundamental assumption behind these methods is that signals that are received by the sparse sensors (or a group of sensors) are coherent. However, coherence may vary significantly with the changes in environmental, terrain, and, operating conditions. In this paper canonical correlation analysis is used to study the variations in coherence between pairs of sub-arrays in a sparse array problem. The data set for this study is a subset of an acoustic signature data set, acquired from the US Army TACOM-ARDEC, Picatinny Arsenal, NJ. This data set is collected using three wagon-wheel type arrays with five microphones. The results show that in nominal operating conditions, i.e. no extreme wind noise or masking effects by trees, building, etc., the signals collected at different sensor arrays are indeed coherent even at distant node separation.

关键词： DOA estimation sparse sensor arrays signal coherence canonical coordinates

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Wideband DOA estimation algorithms for multiple target detection and tracking using unattended acoustic sensors

Wideband DOA estimation algorithms for multiple target detec...

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Conference on Unattended/Unmanned Ground, Ocean and Air sensor Technologies and Applications VI

作者： Azimi-Sadjadi, MR Pezeshki, A Scharf, LL Hohil, M Informat Syst Technol Inc Ft Collins CO 80521 USA

ISBN: (纸本)0819453404

The problem of detection, tracking and localization of multiple wideband sources (ground vehicles) using unattended passive acoustic sensors is considered in this paper. Existing methods typically fail to detect, resolve and track multiple closely spaced sources in tight formations, especially in the presence of clutter and wind noise. In this paper, several existing wideband direction of arrival (DOA) estimation algorithms are extended and applied to this problem. A modified version of the Steered Covariance Matrix (STCM) algorithm is presented that uses a two-step search process. To overcome the problems of existing DOA estimation methods, new wideband versions of the narrowband Capon beamforming method are proposed that use various algorithms for combining power spectra from different frequency bins. These methods are then implemented and benchmarked on a real acoustic signature data set that contains multiple ground targets moving in tight formations.

关键词： DOA estimation sparse sensor arrays steered covariance matrix Capon beamforming

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An Adaptive sparse Array Beamforming Algorithm Based on Approximate L0-norm and Logarithmic Cost

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APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL 2021年第7期36卷 838-843页

作者： Wang, Haixu Li, YingSong Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China

This paper introduces a constrained normalized adaptive sparse array beamforming algorithm based on approximate L-0-norm and logarithmic cost (L-0-CNLMLS). The proposed algorithm can control the sparsity of the array by introducing an approximate function of L-0-norm. In addition, the introduction of logarithmic cost improves the stability of the algorithm as well as the convergence rate of the algorithm. The sparsity of the array can be controlled when adjusting related parameter in the proposed algorithm. Simulation results show the better performance of L-0-CNLMLS compared with some conventional algorithms.

关键词： Approximate L-0-norm constrained adaptive beamforming logarithmic cost function sparse sensor arrays

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L₁-Constrained Normalized LMS Algorithms for Adaptive Beamforming

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2015年第24期63卷 6524-6539页

作者： de Andrade, Jose Francisco, Jr. de Campos, Marcello L. R. Apolinario, Jose Antonio, Jr. Univ Fed Rio de Janeiro Programa Engn Eletr BR-21941901 Rio De Janeiro Brazil Mil Inst Engn IME Dept Elect Engn SE 3 BR-21941901 Rio De Janeiro Brazil

We detail in this paper an L-1-norm Linearly constrained normalized least-mean-square (L-1-CNLMS) algorithm and its weighted version (L-1-WCNLMS) applied to solve problems whose solutions have some degree of sparsity, such as the beam-forming problem in uniform linear arrays, standard hexagonal arrays, and (non-standard) hexagonal antenna arrays. In addition to the linear constraints present in the CNLMS algorithm, the L-1-WCNLMS and the L-1-CNLMS algorithms take into account an L-1-norm penalty on the filter coefficients, which results in sparse solutions producing thinned arrays. The effectiveness of both algorithms is demonstrated via computer simulations. When employing these algorithms to antenna array problems, the resulting effect due to the -norm constraint is perceived as a large aperture array with few active elements. Although this work focuses the algorithm on antenna array synthesis, its application is not limited to them, i.e., the L-1-CNLMS is suitable to solve other problems like sparse system identification and signal reconstruction, where the weighted version, the L-1-WCNLMS algorithm, presents superior performance compared to the L-1-CNLMS algorithm.

关键词： CNLMS algorithm L-1-norm sparse sensor arrays constrained adaptive beamforming thinned arrays

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