Fault Diagnosis of Planar Antenna Arrays Using Neural Networks

Vakula, D.; Sarma, N. V. S. N.

doi:10.2528/pierm09011204

Cited by 18 publications

(13 citation statements)

References 6 publications

(5 reference statements)

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“…In the recent past, several methods have been proposed for detection of such faults. Methods based on Genetic Algorithms (GA) [2], Neural Networks (NN) [3], Bacterial Foraging Optimization (BFO) [4], Bayesian Compressive Sensing [5], Exponentially Weighted Moving Average Scheme (EWMA) [6], etc. were proposed for fault detection, but no method has been proposed for precise location of the error in the array.…”

Section: Introductionmentioning

confidence: 99%

Properties and Applications of Error Coefficient Matrix in Linear Antenna Array Design

Appasani¹

2016

PIER M

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Abstract-This paper presents the theoretical framework for a new technique in the field of linear antenna arrays with amplitude control called error coefficient matrix. First of all, the array factor is expressed as a summation of contribution from the elements of the array. It will be shown that for small errors in excitation amplitude, the error in the overall radiation pattern at a given angle is a summation of errors contributed by the individual elements of the array at that angle. An error coefficient matrix is proposed, and its properties are discussed in great detail. The accuracy of the proposed method is investigated for varying levels of errors in weights and for varying number of error elements, using Monte-Carlo simulation. Finally, the applications of this new technique in the field of antenna arrays are presented.

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Section: Introductionmentioning

confidence: 99%

Properties and Applications of Error Coefficient Matrix in Linear Antenna Array Design

Appasani¹

2016

PIER M

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“…By using ANNs it is possible to recalculate and produce the radiation pattern close to the original pattern [24][25] [26][29] [31][32]. The adaline [24], multilayer perceptron (MLP) [21][ [25][26][33]the radial basis function (RBF) neural networks [31][32]and the discrete mean field neural network based on Vidyasagar net [22] and have been found quite effective in finding faults in the antenna arrays. Genetic algorithms have also been very effectively used in fault diagnosis of arrays as mentioned ahead [20] [21][23] [30].…”

Section: Introductionmentioning

confidence: 99%

“…The possibility of failure in some of them increases due to the large number of elements. Faults in arrays disturb the radiation pattern [20] [21][26] [31] and also affect the input impedance [23].Traditional analytical methods used earlier for locating the faults in antenna arrays are tedious and time consuming. Information on the number and location of the faulty elements is required and these methods generally find it difficult to handle fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…Fault diagnosis of planar antenna arrays using neural networks [31] Radial basis function and Probabilistic neural networks 5x5 and 8x8 planar arrays 13 Using neural networks for fault detection in planar antenna arrays [32] Radial basis function and Probabilistic neural networks 5x5 and 8x8 planar arrays 14 Element failure diagnosis in a planar antenna array by the use of neural networks [33] Multi layer perceptron neural network 6x 6 planar array, d=0.9λ…”

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confidence: 99%

“…The method has proven its effectiveness for completely faulty as well as partially faulty element. The genetic algorithm is normally used to detect the faulty elements in small size arrays [31] but for large arrays it has to be run several times for accurate results. Applications of radial basis function (RBF) and probabilistic neural network (PNN) have been suggested by authors in [31] and [32] using planar array with 5 x 5 and 8 x 8 isotropic elements having uniform excitations and inter element spacing of 0.5λ.…”

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confidence: 99%

See 2 more Smart Citations

A Survey on Applications of Neural Networks and Genetic Algorithms in Fault Diagnostics for Antenna Arrays

Mishra¹

2013

IOSR-JEEE

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Detection and correction of errors in linear antenna arrays

Appasani

Pelluri

Gupta

2018

Int J Numerical Modelling

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Detection and correction of errors is a hot topic of research in the field of antenna arrays. The errors in the excitation amplitude result in the adulteration of the radiation pattern, and hence, the dynamic detection and correction of these errors are very important for proper maintenance of the radiation pattern. In this paper, a novel technique for detection and correction of errors in linear antenna arrays is presented. The analytical nature of the proposed approach makes it a viable alternative to the optimization techniques available in the literature. The error in the radiation pattern measured at an angle is expressed as a function of the individual excitation errors, and subsequently, several useful theorems and techniques are presented in the framework of error detection and correction. An exhaustive mathematical explanation followed by numerical simulations is presented to clearly illustrate the advantage of this method in the detection and correction of errors in antenna arrays.

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Fault Diagnosis of Planar Antenna Arrays Using Neural Networks

Cited by 18 publications

References 6 publications

Properties and Applications of Error Coefficient Matrix in Linear Antenna Array Design

Properties and Applications of Error Coefficient Matrix in Linear Antenna Array Design

A Survey on Applications of Neural Networks and Genetic Algorithms in Fault Diagnostics for Antenna Arrays

Detection and correction of errors in linear antenna arrays

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