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Antennas and Propagation, IEEE Transactions on

Issue 3  Part 1 • Date March 2007

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  • Table of contents

    Publication Year: 2007 , Page(s): C1 - 517
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  • IEEE Transactions on Antennas and Propagation publication information

    Publication Year: 2007 , Page(s): C2
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  • Guest Editorial for the Special Issue on Synthesis and Optimization Techniques in Electromagnetics and Antenna System Design

    Publication Year: 2007 , Page(s): 518 - 522
    Cited by:  Papers (8)
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  • Evolutionary Programming in Electromagnetic Optimization: A Review

    Publication Year: 2007 , Page(s): 523 - 537
    Cited by:  Papers (24)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1463 KB) |  | HTML iconHTML  

    In this paper, we review recent advances in evolutionary programming (EP) and its implementation in various antenna, microwave, frequency selective surfaces and RF circuit optimization problems. EP, unlike the other two paradigms of evolutionary computational techniques, namely, genetic algorithms (GA) and evolution strategies (ES), uses a mutation only variation operator where adaptive and/or self-adaptive techniques exist, or can easily be designed, for adapting the parameters of mutation operator during the evolution process. We present the so-called meta-EP and design of its mutation operator, using Gaussian, Cauchy and Poisson mutations as well as a hybrid of these mutations, for continuous, discrete and mixed parameter electromagnetic optimization problems. In addition, an efficient hybrid use of EP, gradient search methods and cluster analysis, as well as a novel hybrid EP-GA algorithm are discussed. Examples presented include optimizations of corrugated horn antennas, multilayered stacked microstrip antennas, Yagi-Uda arrays, partially reflective surfaces, dielectric filters, meander-line polarizer and RF duplexers View full abstract»

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  • Stochastic Optimization Methods Applied to Microwave Imaging: A Review

    Publication Year: 2007 , Page(s): 538 - 548
    Cited by:  Papers (41)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (478 KB) |  | HTML iconHTML  

    Stochastic methods are now very common in electromagnetics. Among the various applications, they have been recently proposed for solving inverse problems arising in radio-frequency and microwave imaging. Some of the recently proposed stochastic inversion procedures are critically discussed (e.g., genetic algorithms, differential evolution methods, memetic algorithms, particle swarm optimizations, hybrid techniques, etc.) and the way they have been applied in this area. The use of the ant colony optimization method, which is a relatively new method in electromagnetics, is also proposed. Various imaging modalities are considered (tomography, buried object detection, and borehole sensing). Finally, the main features making these approaches useful for imaging purposes are discussed and the currently considered strategies to reduce the computational load associated with stochastic optimization procedures delineated View full abstract»

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  • Comparison of Different Heuristic Optimization Methods for Near-Field Antenna Measurements

    Publication Year: 2007 , Page(s): 549 - 555
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (485 KB) |  | HTML iconHTML  

    A comparison between different modern heuristic optimization methods applied to antenna far-field radiation pattern reconstruction from planar near-field data is presented in this paper. The antenna under test is represented by means of equivalent magnetic currents (EMC) whose components are optimized using several heuristic algorithms such as simulated annealing (SA), genetic algorithms (GA), and particle swarm optimization (PSO), as well as a traditional local optimization method, the Nelder Mead downhill simplex algorithm. Several schemes for GA (classical real-valued and binary encoding, and their hybrid versions) and PSO (global or local topologies with synchronous or asynchronous updates of the swarm) have been considered in the analysis and the pros and cons of each one are reported and discussed. A study of the performance and limitations of each algorithm using as a canonical problem a small size antenna aperture, along with results of near-field to far-field (NF-FF) transformation are also included View full abstract»

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  • Advances in Particle Swarm Optimization for Antenna Designs: Real-Number, Binary, Single-Objective and Multiobjective Implementations

    Publication Year: 2007 , Page(s): 556 - 567
    Cited by:  Papers (137)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2077 KB) |  | HTML iconHTML  

    The particle swarm optimization (PSO) is a recently developed evolutionary algorithm (EA) based on the swarm behavior in the nature. This paper presents recent advances in applying a versatile PSO engine to real-number, binary, single-objective and multiobjective optimizations for antenna designs, with a randomized Newtonian mechanics model developed to describe the swarm behavior. The design of aperiodic (nonuniform and thinned) antenna arrays is presented as an example for the application of the PSO engine. In particular, in order to achieve an improved peak sidelobe level (SLL), element positions in a nonuniform array are optimized by real-number PSO (RPSO). On the other hand, in a thinned array, the on/off state of each element is determined by binary PSO (BPSO). Optimizations for both nonuniform arrays and thinned arrays are also expanded to multiobjective cases. As a result, nondominated designs on the Pareto front enable one to achieve other design factors than the peak SLL. Optimized antenna arrays are compared with periodic arrays and previously presented aperiodic arrays. Selected designs fabricated and measured to validate the effectiveness of PSO in practical electromagnetic problems View full abstract»

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  • A Microparticle Swarm Optimizer for the Reconstruction of Microwave Images

    Publication Year: 2007 , Page(s): 568 - 576
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (674 KB) |  | HTML iconHTML  

    A novel optimization technique known as the microparticle swarm optimizer (muPSO) is proposed for high-dimensional microwave image reconstruction. With the proposed muPSO, good optimization performance can be obtained especially for solving high-dimensional optimization problems. In addition, the proposed muPSO requires only a small population size to outperform the standard PSO that uses a larger population size. Our simulation results on the reconstruction of the dielectric properties of normal and malignant breast tissues have shown that the muPSO can perform quite well for this high-dimensional microwave image reconstruction problem View full abstract»

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  • Antenna Design With a Mixed Integer Genetic Algorithm

    Publication Year: 2007 , Page(s): 577 - 582
    Cited by:  Papers (33)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (800 KB) |  | HTML iconHTML  

    Antenna design variables, such as size, have continuous values while others, such as permittivity, have a finite number of values. Having both variable types in one problem requires a mixed integer optimization algorithm. This paper describes a genetic algorithm (GA) that works with real and/or binary values in the same chromosome. The algorithm is demonstrated on designing low side-lobe phase tapers, circularly polarized patch antennas, and identically thinned subarrays View full abstract»

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  • An Autopolyploidy-Based Genetic Algorithm for Enhanced Evolution of Linear Polyfractal Arrays

    Publication Year: 2007 , Page(s): 583 - 593
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (810 KB) |  | HTML iconHTML  

    There has been considerable recent interest in techniques for the optimization of large-N antenna arrays. Unfortunately, the successful development of such techniques has been hindered by the large number of independent parameters that must be optimized and the complexity of the calculations needed for the electromagnetic evaluation of large-N arrays. One promising new design methodology for large-N arrays which has recently been introduced is based on properties of a subset of fractal-random arrays called polyfractal arrays. Polyfractal arrays have many embedded self-similar structures, thereby allowing very large and seemingly complex array layouts to be described with only a small set of independent parameters. In addition, by effectively utilizing the self-similarity of polyfractal arrays, a considerable reduction can be achieved in the amount of time required to evaluate the radiation patterns of large-N arrays. This paper introduces a type of nature-based design process that applies a specially formulated genetic algorithm (GA) technique to evolve optimal polyfractal array layouts. The most unique aspect of this optimization technique is a new autopolyploidy-based chromosome expansion that maximizes the efficiency of the GAs. Simple polyfractal geometries are used in the initial stage or first epoch of the optimization because the number of independent parameters is small and the computation times are relatively fast. After the optimization converges for the first epoch, more complicated descriptions of these polyfractal arrays are introduced to provide additional independent parameters for the optimizer as it progresses through later epochs of evolution. This process has been shown to be very effective in creating optimized large-N arrays, the largest example considered here being a 1616-element linear array with a -24.30-dB sidelobe level and a 0.056deg half-power beamwidth View full abstract»

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  • Multiobjective Optimal Antenna Design Based on Volumetric Material Optimization

    Publication Year: 2007 , Page(s): 594 - 603
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1390 KB) |  | HTML iconHTML  

    There is growing interest for small antennas that concurrently have higher functionality and operability. Multiobjective optimization is an important tool in the design of such antennas since conflicting goals such as higher gain, increased bandwidth, and size reduction must be addressed simultaneously. In this paper, we present a novel optimization algorithm which permits full volumetric antenna design by combining genetic algorithms with a fast hybrid finite element-boundary integral method. To our knowledge, this is the first time that a full three dimensional antenna design is pursued using concurrent shape, size, metallization as well as dielectric and magnetic material volume optimization. In comparison to previous optimization pursuits, our approach employs a wide-frequency sweep using a single geometry model, thus, enhancing speed, along with several discrete material choices for realizable optimized designs. The developed algorithm can be interpreted as a three dimensional Pareto optimization scheme and provides the designer with several choices among the best antennas, according to design goals and constraints. The final designs are associated with very thin (~0.01lambda) material substrates and yield as much as 15% bandwidth using a 0.1lambda--0.4lambda aperture subject to various gain and bandwidth requirements View full abstract»

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  • Design of a Band-Notched Planar Monopole Antenna Using Genetic Algorithm Optimization

    Publication Year: 2007 , Page(s): 604 - 610
    Cited by:  Papers (35)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (933 KB) |  | HTML iconHTML  

    Genetic algorithm (GA) optimization is applied to the design of planar monopole antennas, which exhibit both ultrawideband (UWB) operation and a narrow-band frequency notch. Such an antenna is useful in applications involving wideband communications where it is desired to mitigate interference with other radio systems colocated with the operating band. It is demonstrated in this paper that traditional band-notched planar monopole antennas exhibit asymmetry in radiation patterns within the notch band such that the attenuation provided by the antenna varies as a function of azimuth angle, which lowers the effective bandwidth of the notch. A GA optimizer, which uses of a weighted sum cost function related to impedance matching and radiation patterns at frequencies within both the wide operating band and narrow notch band, is used to improve the performance of the band-notch planar monopole antenna. A two-dimensional (2-D) matrix chromosome is used in the GA to represent a wide-range on planar element shapes. It is shown that the GA generates antenna designs that exhibit equal wideband performance as traditional band-notched designs, but have improved azimuth plane radiation pattern symmetry, which widens the effective notch bandwidth. The GA-generated antenna design is measured and compared with simulation View full abstract»

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  • Broadband HF Antenna Matching Network Design Using a Real-Coded Genetic Algorithm

    Publication Year: 2007 , Page(s): 611 - 618
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (749 KB) |  | HTML iconHTML  

    A real-coded genetic algorithm was successfully applied to the matching network problem. Our approach reduces the search across different network topologies by the means of a generalized network description, in which the algorithm "decides" whether a reactive element is inductive or capacitive. Compared to previous analytical and semianalytical techniques that do not require a priori information about the network topology, this method is considerably simpler and its application is straightforward, regardless of the network complexity. Our approach accounts for constrained lossy elements, tolerances, and network on-site tuning. The results for a canonical load show the effectiveness of this method in comparison with other known methods. Finally, for HF broadband antennas, which involve network complexity limits and component value restrictions, it will be shown that the use of lossy networks may help to fulfil system requirements, with only a moderate impact on system efficiency View full abstract»

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  • A Distributed Intelligent Agent Platform for Genetic Optimization in CEM: Applications in a Quasi-Point Matching Method

    Publication Year: 2007 , Page(s): 619 - 628
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1073 KB) |  | HTML iconHTML  

    The use of genetic algorithms in computational electromagnetics (CEM) has proved a fruitful, but resource demanding optimization method with numerous practical applications. This paper introduces a parallel distributed computing framework based on the intelligent agent technology that is capable of handling the genetic optimization for diverse CEM problems. The platform core component is the genetic search agent (GSA), a collaborative computational entity that communicates with its peers in order to carry out a genetic optimization scheme. The platform can interface with foreign codes transparently due to its flexible code loading mechanisms, specially designed for CEM applications. The framework is applied to the optimization of a quasi-point matching method with fictitious sources (QPM-FS). The analysis comprises two case studies, involving electromagnetic scattering by: i) a two-layered cylinder; and ii) a cylinder buried in a two-layered earth medium, a problem of practical use in subsurface imaging. The performance results indicate that the sophisticated distribution mechanisms achieve high speed-up, while physically intuitive conclusions are obtained from the genetic optimization of the developed QPM-FS method View full abstract»

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  • Geometry Reconstruction of Conducting Cylinders Using Genetic Programming

    Publication Year: 2007 , Page(s): 629 - 636
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (347 KB) |  | HTML iconHTML  

    A genetic programming-based method for the imaging of two-dimensional conductors is presented. Geometry is encoded in this scheme using a tree-shaped chromosome to represent the Boolean combination of convex polygons into an arbitrary two-dimensional geometry. The polygons themselves are encoded as the convex hull of variable-length lists of points that reside in the terminal nodes of the tree. A set of genetic operators is defined for efficiently solving the inverse scattering problem. Specifically, the encoding scheme allows for a standard genetic programming crossover operator, and several mutation operators are designed in consideration of the encoding scheme. Several results are presented that demonstrate the method on a number of different shapes View full abstract»

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  • Optimizing Antenna Array Geometry for Interference Suppression

    Publication Year: 2007 , Page(s): 637 - 641
    Cited by:  Papers (20)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (321 KB) |  | HTML iconHTML  

    The determination of an optimum antenna array geometry for suppressing interference is addressed. An optimization problem is derived whose solution yields an optimum array geometry for a given interference environment. A specific example is solved via the Simulated Annealing Optimization algorithm and results are presented for arrays of varying configurations View full abstract»

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  • Kernel Antenna Array Processing

    Publication Year: 2007 , Page(s): 642 - 650
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (629 KB) |  | HTML iconHTML  

    We introduce two support vector machine (SVM)-based approaches for solving antenna problems such as beamforming, sidelobe suppression, and maximization of the signal-to-noise ratio. A basic introduction to SVM optimization is provided and a complex nonlinear SVM formulation developed to handle antenna array processing in space and time. The new optimization formulation is compared with both the minimum mean square error and the minimum variance distortionless response methods. Several examples are included to show the performance of the new approaches View full abstract»

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  • Antenna Optimization Through Space Mapping

    Publication Year: 2007 , Page(s): 651 - 658
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (935 KB) |  | HTML iconHTML  

    We apply space mapping to antenna design for the first time. We exploit a coarse-mesh method of moments (MoM) solver as the coarse model and align it with the fine-mesh MoM solution through space mapping. We employ two plans: (I) implicit and output space mapping, and (II) input and output space mapping. We propose a local meshing method which avoids inconsistencies in the coarse model. The proposed techniques are implemented through our user-friendly space mapping framework (SMF) system. In a double annular ring antenna example, the S-parameter is optimized. The finite ground size effect for the MoM is efficiently solved by space mapping plan I and the design specification is satisfied after only three iterations. In a patch antenna example, we optimize the impedance using both plans in separate optimization processes. Comparisons are made. Coarseness in the coarse model and its effect on the space mapping performance are also discussed View full abstract»

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  • A Hybrid Method Based on Combining Artificial Neural Network and Fuzzy Inference System for Simultaneous Computation of Resonant Frequencies of Rectangular, Circular, and Triangular Microstrip Antennas

    Publication Year: 2007 , Page(s): 659 - 668
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (698 KB) |  | HTML iconHTML  

    A hybrid method based on a combination of artificial neural network (ANN) and fuzzy inference system (FIS) is presented to calculate simultaneously the resonant frequencies of various microstrip antennas (MSAs) of regular geometries. The ANN is trained with the Bayesian regulation algorithm. An algorithm that integrates least square method and backpropagation algorithm is used to identify the parameters of FIS. The resonant frequency results of the proposed hybrid method for the rectangular, circular, and triangular MSAs are in very good agreement with the experimental results available in the literature View full abstract»

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  • Application of Artificial Neural Networks to Broadband Antenna Design Based on a Parametric Frequency Model

    Publication Year: 2007 , Page(s): 669 - 674
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1058 KB) |  | HTML iconHTML  

    An artificial neural network (ANN) is proposed to predict the input impedance of a broadband antenna as a function of its geometric parameters. The input resistance of the antenna is first parameterized by a Gaussian model, and the ANN is constructed to approximate the nonlinear relationship between the antenna geometry and the model parameters. Introducing the model simplifies the ANN and decreases the training time. The reactance of the antenna is then constructed by the Hilbert transform from the resistance found by the neuromodel. A hybrid gradient descent and particle swarm optimization method is used to train the neural network. As an example, an ANN is constructed for a loop antenna with three tuning arms. The antenna structure is then optimized for broadband operation via a genetic algorithm that uses input impedance estimates provided by the trained ANN in place of brute-force electromagnetic computations. It is found that the required number of electromagnetic computations in training the ANN is ten times lower than that needed during the antenna optimization process, resulting in significant time savings View full abstract»

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  • Projection Matrix Method for Shaped Beam Synthesis in Phased Arrays and Reflectors

    Publication Year: 2007 , Page(s): 675 - 683
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (894 KB) |  | HTML iconHTML  

    We present the projection matrix method for shaped beam synthesis in a general array of different size elements. The method relies on orthogonal projection of the desired far-field intensity vector onto the space spanned by the far-field intensity vectors of the array elements. It is found that for a uniform convergence of the solution, the far-field sample space must be extended beyond the coverage region; otherwise the projection matrix becomes ill conditioned. A general guideline for the far-field sample space is provided. The method, with necessary amendments, is then employed successfully for a reflector surface synthesis. The method is found to be several times faster than the gradient search method commonly used for beam synthesis. Numerical results for array and shaped reflector syntheses are shown and the advantages discussed View full abstract»

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  • A Multiresolution Approach to Contoured-Beam Antennas

    Publication Year: 2007 , Page(s): 684 - 697
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1890 KB) |  | HTML iconHTML  

    In the design of contoured-beam antennas, several synthesis methods have been developed, but usually little importance has been given to the issue of finding an "efficient" representation of the antenna far field: this is the main scope of this paper. A wavelet expansion of the far field is developed and applied to the synthesis of array-fed reflector antennas for geographic coverage (e.g., Europe coverage). Two different synthesis techniques are considered as application examples of the devised pattern representation: a field-synthesis projection method and a power synthesis optimization based on a genetic algorithm specifically adapted for antenna design. As a byproduct, this paper also presents a way to construct a locally orthogonal beam basis that can be used without the present wavelet scheme View full abstract»

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  • Optimizing Arrays of Randomly Placed Wireless Transmitters for Receivers Located Within the Array Volume

    Publication Year: 2007 , Page(s): 698 - 706
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (962 KB) |  | HTML iconHTML  

    We investigate the potential of using arbitrarily placed wireless transceivers to increase the probability of maintaining a communication link in an electrically harsh environment. Specifically, we adapt a well-known matrix-based array optimization technique to the case when the transmitting elements exist in a complex environment and the receiver is not in the far-field of the array. Study of array performance in a non-ideal setting represents an important step in determining the feasibility of using this optimization technique for ad hoc wireless arrays within a building. Measures of array performance consist of median values for the directivity or gain, the total power at the receiver location, and the power per transmitter. The simulation results include array performance in the presence of a lossy dielectric corner to study the effects of building floors and walls. Our results show the median of the optimized directivity or gain for the frequencies of interest with simple boundaries is within 3 dB of that for the optimized configuration in free space View full abstract»

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  • Nonconventional Least Squares Optimization for DOA Estimation

    Publication Year: 2007 , Page(s): 707 - 714
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1283 KB) |  | HTML iconHTML  

    An optimization technique based on the nonconventional least squares approximation to the direction-of-arrival (DOA) estimation problem is presented. In contrast to the conventional least squares problem, where the number of equations is greater than the number of unknowns, in this nonconventional optimization procedure, the number of unknowns is much greater than the number of equations and hence it is a very underdetermined problem. The proposed method utilizes signal steering vector as a function of azimuth angles similar to the discrete Fourier transform (DFT) concept. Various electromagnetic effects, such as mutual coupling between array elements, antenna element failure, the use of dissimilar antenna elements, the use of nonplanar and nonuniformly spaced array elements, and coupling from near-field scatterers can be automatically taken into account in the preprocessing. After carrying out the electromagnetic optimization through a preprocessing, the DOA estimation reduces to a simple matrix multiplication, which reduces the computational complexity in the estimation. Hence, this procedure is ideally suited for deployment in a complex environment and the entire computation can be done in real time. Sample numerical results are presented to demonstrate the performance and accuracy of this procedure. This is a good procedure for the DOA estimation but not very accurate in estimation of the amplitudes due to the classical picket fence effect produced by a DFT-based methodology View full abstract»

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  • Fast Low-Sidelobe Synthesis for Large Planar Array Antennas Utilizing Successive Fast Fourier Transforms of the Array Factor

    Publication Year: 2007 , Page(s): 715 - 722
    Cited by:  Papers (24)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1972 KB) |  | HTML iconHTML  

    A new and very fast low-sidelobe pattern synthesis method for planar array antennas with periodic element spacing is described. The basic idea of the method is that since the array factor is related to the element excitations through an inverse Fourier transform, the element excitations can be derived from the array factor through a direct Fourier transform. Starting with an initial set of suitable element excitations the array factor is calculated. After matching the array factor to the prescribed pattern, an updated set of excitations is obtained through a direct Fourier transform performed on the matched array factor. From this updated set, only the samples associated with the aperture are retained, where after a new array factor is calculated. The whole process is repeated until the updated array factor does not violate any longer the pattern requirements. The proposed synthesis method provides significant improvements in terms of performance, computational speed, flexibility, and ease of implementation in software to the methods described in reviewed literature. A number of representative examples are presented to demonstrate the various unique capabilities of the method. The results include sum and difference patterns for circular and elliptical aperture shapes featuring uniform ultra low sidelobes View full abstract»

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IEEE Transactions on Antennas and Propagation includes theoretical and experimental advances in antennas.

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Meet Our Editors

Editor-in-Chief                                                 Kwok W. Leung