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Optical synthetic aperture circle-array optimization based on genetic algorithm

Frontiers of Optoelectronics 2008年第Z2期1卷 268-273页

作者： Yuntao HE Yuesong JIANG Guangda LIU School of Electronic Information Engineering Beihang University

An optimization model of circle array was set up from the basic optical synthetic aperture imaging principle. The circle array was optimized by adopting a genetic algorithm with an improved real coding method coding the location of sub-apertures. The measure function was designed based on maximizing the distances between u-v coverage dots and minimizing the redundant array. The point spread function, optical transfer function and diffractive imaging were analyzed with the circle array synthetic aperture imaging *** optimized result of 8 to 16 sub-apertures on a circle array was obtained, and they were compared to the results achieved through simulated annealing *** the emulator program, the point spread function was analyzed and contrasted to that of a uniform circle array. Results show that the real coding genetic algorithm can resolve the array optimization well, cost less time and get a better optimization compared with the simulated annealing algorithm.

关键词： synthetic aperture array optimization genetic algorithm u-v coverage real coding

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Multilook SAR processing and array optimization applied to radio echo ice sounding data

Multilook SAR processing and array optimization applied to r...

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IEEE International Symposium on Phased array Systems and Technology (PAST)

作者： Mishra, Akhilesh Yan, Jie-Bang Leuschen, Carlton J. Gogineni, S. Univ Kansas Ctr Remote Sensing Ice Sheets CReSIS Lawrence KS 66045 USA

ISBN: (纸本)9781509014477

Several outlet glaciers in Greenland are retreating and speeding up, and Greenland ice sheet's contribution to sea level is increasing over the last decade. Information on ice thickness and bed conditions is needed in order to understand why these outlet glaciers are retreating and speeding up, and to develop both diagnostic and prognostic models. These outlet glaciers contain very lossy ice and have a very-rough crevassed surface. Airborne sounding of these fast-moving outlet glaciers is very difficult because extremely weak bed echoes are often buried in off-nadir clutter - off-nadir scattered signals from the rough surface. We processed data from each element of the antenna-array with a multilook time-domain back-projection (MLTDBP) synthetic aperture radar (SAR) processor and used these data in an adaptive array processing algorithm we developed to extract weak bed echoes buried in clutter and noise. The algorithm is based on the gradient descent method. It estimates and minimizes phase and amplitude errors introduced by aircraft roll, the radar system, and aircraft wing flexure. We will show a few examples of recovered bed echoes from data collected over glaciers near Nuuk, Greenland during the 2009 field season to demonstrate the performance of the MLTDBP processor and array optimization algorithm. We also included an example from data collected on the interior ice during 2006.

关键词： array signal processing SAR array optimization airborne radar

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array optimization of sparse regularization equivalent source acoustic holography algorithm

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JOURNAL OF VIBROENGINEERING 2021年第5期23卷 1244-1253页

作者： Xia, Jing Chen, Hantao Guo, Wenyong Han, Jianggui Naval Univ Engn Coll Power Engn Wuhan Peoples R China

In order to improve the accuracy of the sparse regularization equivalent source acoustic holography algorithm, based on the analysis of the holographic algorithm theory, an optimized array arrangement is proposed. The sensing matrix constructed by the array parameters directly affects the accuracy of the acoustic imaging algorithm. By analyzing the influence of the sensing matrix on the imaging algorithm, the Restricted Isometry Constant (RIC) is chosen to evaluate the sensing matrix. Using genetic algorithm (GA), the RIC is taken as the fitness value, and the optimal pseudo-random array is selected and compared with the conventional array arrangement for acoustic imaging. Experiments show that the optimized pseudo-random array has better imaging effect under the same number of sensor measurements, and provides an optimization method for the design of acoustic array.

关键词： equivalent source acoustic holography array optimization Monte-Carlo method sensing matrix

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Sensor array optimization for the electronic nose via different deep learning methods

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SENSORS AND ACTUATORS B-CHEMICAL 2024年 410卷

作者： Zhang, Xijia Wang, Tao Ni, Wangze Zhang, Yongwei Lv, Wen Zeng, Min Yang, Jianhua Hu, Nantao Zhan, Rui Li, Guang Hong, Zhiqiang Yang, Zhi Shanghai Jiao Tong Univ Inst Marine Equipment Sch Elect Informat & Elect Engn Dept Micro Nano ElectMinist Educ Shanghai 200240 Peoples R China Dongfeng Motor Corp Wuhan 430058 Peoples R China

A sensor array is a key component of an electronic nose (E-nose). However, the practical applications of the Enose are often inhibited by its size and energy consumption arising from the number of gas sensors. Achieving a high-performance E-nose with a minimum number of sensors is key and challenging for its practical applications. In this study, different machine learning models have been studied and compared to optimize the performance of the E-nose. The results show that the convolutional neural network (CNN) is the best-performing model, which has an accuracy of 0.986 for classification, and an R-square score of 0.979 for concentration prediction, outperforming the gated recurrent unit, long short-term memory, multi-layer perceptron neural network, and support vector machine. The performance of the E-nose experiences only a minor decrease when the number of sensors that participated in pattern recognition is reduced from eight to four, where the CNN model can yield an accuracy of 0.905 for classification and an R-square of 0.972 for concentration prediction. To further quantify the pros and cons of array optimization, a cost-effective metric is designed to reveal the suitable array size under different scenarios. This work can provide valuable guidance in the design of portable E-nose devices with a smaller size and optimal performance.

关键词： Electronic nose array optimization Convolutional neural network Recurrent neural network Deep learning Pattern recognition

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Arbitrary Microphone array optimization Method Based on TDOA for Specific Localization Scenarios

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SENSORS 2019年第19期19卷 4326页

作者： Liu, Haitao Kirubarajan, Thia Xiao, Qian East China Jiaotong Univ Sch Mechatron & Vehicle Engn Nanchang 330013 Jiangxi Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4K1 Canada Bldg 13 South Dist808 Shuanggang East St Nanchang 330013 Jiangxi Peoples R China

Various microphone array geometries (e.g., linear, circular, square, cubic, spherical, etc.) have been used to improve the positioning accuracy of sound source localization. However, whether these array structures are optimal for various specific localization scenarios is still a subject of debate. This paper addresses a microphone array optimization method for sound source localization based on TDOA (time difference of arrival). The geometric structure of the microphone array is established in parametric form. A triangulation method with TDOA was used to build the spatial sound source location model, which consists of a group of nonlinear multivariate equations. Through reasonable transformation, the nonlinear multivariate equations can be converted to a group of linear equations that can be approximately solved by the weighted least square method. Then, an optimization model based on particle swarm optimization (PSO) algorithm was constructed to optimize the geometric parameters of the microphone array under different localization scenarios combined with the spatial sound source localization model. In the optimization model, a reasonable fitness evaluation function is established which can comprehensively consider the positioning accuracy and robustness of the microphone array. In order to verify the array optimization method, two specific localization scenarios and two array optimization strategies for each localization scenario were constructed. The optimal array structure parameters were obtained through numerical iteration simulation. The localization performance of the optimal array structures obtained by the method proposed in this paper was compared with the optimal structures proposed in the literature as well as with random array structures. The simulation results show that the optimized array structure gave better positioning accuracy and robustness under both specific localization scenarios. The optimization model proposed could solve the problem of arr

关键词： source localization TDOA array optimization PSO

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MIMO array Capacity optimization Using a Genetic Algorithm

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2011年第6期60卷 2471-2481页

作者： Binelo, Manuel O. de Almeida, Andre L. F. Cavalcanti, F. Rodrigo P. Univ Fed Ceara Wireless Telecom Res Grp BR-60455760 Fortaleza CE Brazil Univ Cruz Alta BR-98025810 Cruz Alta RS Brazil

This paper discusses the design of multiple-input-multiple-output (MIMO) antenna systems and proposes a genetic algorithm to obtain the position and orientation of each MIMO array antenna that maximizes the ergodic capacity for a given propagation scenario. One challenging task in the MIMO system design is to accommodate the multiple antennas in the mobile device without compromising the system capacity, due to spatial and electrical constraints. Based on an interface between the antenna model and the propagation channel model, the ergodic capacity is considered as the objective function of the MIMO array optimization. Simulation results corroborate the importance of polarization and antenna pattern diversities for MIMO in small terminals. Our results also show that the electromagnetic coupling effect can be exploited by the optimizer to decrease signal correlation and increase MIMO capacity. A comparison among a uniform linear array (ULA), a uniform circular array (UCA), and a genetic algorithm (GA)-optimized array is also carried out, showing that the topology given by the optimizer is superior to that of the standard ULA and UCA for the considered propagation channel.

关键词： array optimization capacity genetic algorithm (GA) multiple-input-multiple-output (MIMO)

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Sub-TeraHertz Modular array Layout optimization Under Fabrication Constraints

Sub-TeraHertz Modular Array Layout Optimization Under Fabric...

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Joint Conference of European Conference on Networks and Communications (EuCNC) and 6G Summit (6G Summit)

作者： Dehkordi, Saeid K. Schwanitz, Oliver Marandi, Mostafa Khalili Kaiser, Michael P. Thi Huyen Le Ndip, Ivan Caire, Giuseppe Tech Univ Berlin Dept Informat Theory & Commun Berlin Germany Fraunhofer IZM Berlin Dept RF & Smart Sensor Syst Berlin Germany Tech Univ Dresden Vodafone Chair Mobile Comm Syst Dresden Germany

ISBN: (纸本)9781665498715

Millimeter wave (mmWave) and sub-TeraHertz (sub-THz) multi-user multiple-input multiple-output (MU-MIMO) communications operating in the frequency spectrum (30-300 GHz) have already been identified as the most promising candidate for the second phase of 5G and Beyond (5GB) wireless systems aiming to achieve broadband data communications at rates higher than 1 Gb/s while operating in very dense urban small-cell environments. Due to the presence of strong isotropic pathloss in mmWave/sub-THz frequencies, high antenna gains realized through large antenna arrays will be required to mitigate these effects. The small wavelengths under which mmWave and s-THz systems operate, allow for integration of more compact antenna elements. However, the fabrication of such antennas poses challenges such as positioning tolerance. In this work, we introduce a novel modular array synthesis approach and further investigate the effect of non-ideal element/module positions within an array and provide an optimization framework to minimize the expected synthesized array pattern error taking positioning tolerances into account. Furthermore, we use the realized element pattern of the proposed D-Band patch element with an RF bandwidth of over 10 GHz to demonstrate the results of the proposed optimization algorithm.

关键词： array optimization MIMO Communications sub-TeraHertz/mmWave Communication 6G

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A Low Side-lobe array Weighted optimization Method in Sky-Wave Radar

A Low Side-lobe Array Weighted Optimization Method in Sky-Wa...

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5th IEEE International Symposium on Phased array Systems and Technology

作者： Wang, Zixi He, Zishu Hu, Jinfeng Xie, Julan

ISBN: (纸本)9781467311274

The typical Sky-Wave Radar receive array require extensive leveling of the terrain in front of antenna for low side-lobe performance. This results in significant costs and may bring environmental crises. Thus, a special sparse array system is commonly adopted in this situation, which is able to avoid engineering cost problem by deploying the array elements only in the relative flat terrain and discarding them in the rough segment. However, the sparse array system commonly brings the degradation of the array side-lobe performance which will cause the target detection problems. In this paper, in order to refine the side-lobe performance, a method to distribute the array elements based on the genetic algorithm for rough terrain arraying is presented first. Then a low side-lobe optimization based on the sub-array weighting method is proposed. The simulation results proves that the arraying and weighting optimization method is able to effectively reduce the side-lobe level for the array on rough terrain.

关键词： Sky-Wave Radar array optimization rough terrain

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array optimization and Preprocessing Techniques for Chemical Sensing Microsystems

Sensors Update

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Sensors Update 2002年第1期10卷

作者： D.M. Wilson S. Garrod S. Hoyt S. McKennoch K.S. Booksh University of Washington Seattle WA USA Arizona State University Tempe AZ USA

Physical sensors, defined by their direct chemical interaction with the sensing environment, are a valuable and often essential contribution to the solution of stringent chemical sensing problems. Methods for conditioning signals from these sensors to optimize their presentation to subsequent decision-making models in the signal processing flow are presented. The assembly of sensors into an array and the preprocessing of these signals are the two primary techniques for conditioning a chemical image for concentration detection, chemical discrimination, and, in some cases, odor localization. array optimization involves locating the point at which adding additional sensors to an array generates more noise than information and is highly dependent on the application and number of analytes to be sensed or differentiated. Signal preprocessing techniques include noise reduction, feature extraction, the reduction of array inputs, and the scaling of individual sensor signals and provide a means by which to reconstruct an array of chemical sensor signals into a subset of information that enables a decisionmaking model to do its job with greater efficiency and accuracy. In this chapter, surveys of methods are accompanied by representative examples employing a wide variety of sensors including ChemFETs, chemiresistors, acoustic wave devices, and surface plasmon resonance sensors.

关键词： chemical sensors gas sensors electronic nose preprocessing array optimization

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Colocated MIMO Radar Waveform-array Joint optimization for Sparse array

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SENSORS 2023年第9期23卷 4375页

作者： Yin, Jinrong Ma, Rui Lin, Mingcong Zhou, Shenghua Nanjing Inst Elect Technol Nanjing 210039 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

Colocated multiple-input multiple-output (MIMO) radar can transmit a group of distinct waveforms via its colocated transmit antennas and the waveform diversity leads to several advantages in contrast to conventional phased-array radar. The performance depends highly on the degrees available, and element spacing can be deemed as another source of degrees of freedom. In this paper, we study the joint waveform and element spacing optimization problem. A joint waveform and array optimization criterion is proposed to match the transmit beampattern, the suppression range, and the angular sidelobes, under the constraints of minimal element spacing and total array aperture. Meanwhile, the effect of receive beamforming on suppressing mutual correlation between returns from different spatial directions is also incorporated into the optimization criterion. The optimization problem is solved by the sequential quadratic programming algorithm. Numerical results indicate that with more degrees of freedom from array spacings, colocated MIMO radar achieves a better transmit beampattern matching performance and a lower sidelobe level, compared with a fixed half-wavelength spaced array, but the benefits from additional degrees of freedom from array spacing optimization have a limit.

关键词： colocated MIMO radar waveform optimization array optimization sparse array range sidelobe suppression

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