Rapid Method for Finding Faulty Elements in Antenna Arrays Using Far Field Pattern Samples

Rodríguez‐González, J. A.; Ares‐Pena, F.; Fernández-Delgado, Manuel; Iglesias, Roberto; Barro, Senén

doi:10.1109/tap.2009.2019915

Cited by 54 publications

(35 citation statements)

References 14 publications

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“…The idea behind these approaches is the use of inteferometric algorithms that compute the discrepancies in the amplitude and phase, based on the measurement of interference between the antenna array under test and a reference source, whose radiation characteristics are known a prior. These approaches are only applicable prior to the installation of the array and also have a serious limitation of spectral overlapping that led to the adoption of the far field data‐based array diagnostics . Genetic algorithms, neural networks, bacterial foraging optimization, simulated annealing, case‐based reasoning, Bayesian compressive sensing, particle swarm optimization, exponentially weighted moving average scheme, etc were incorporated for the purpose of fault detection.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

Detection and correction of errors in linear antenna arrays

Appasani

Pelluri

Gupta

2018

Int J Numerical Modelling

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“…This problem can be solved easily by replacing the faulty sensors in ground scenario but it is very difficult in case of satellite to replace the faulty sensors. In literature the commonly used techniques for array detection are matrix method [5], back propagation algorithm [6] and exhaustive searches [7]. The solution of array diagnosis problem is reported in [8] using neural networks.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Faulty Sensors in Phased Array Radar Using Compressed Sensing and Hybrid IRLS–SSF Algorithm

Khan

Qureshi

Haider

et al. 2016

Wireless Pers Commun

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In this work, a compressed sensing technique for diagnosis of faulty sensor in an array antenna is proposed. This technique starts from collecting the measurement of the far field pattern. The system relating the difference among the field measured using the healthy reference array and the field radiated by the array under test is analyzed using a parallel coordinate descent (PCD) algorithm, separable surrogate functionals (SSF) algorithm, iterative-reweighted-least-squares (IRLS) algorithm and hybrid IRLS-SSF. These algorithms are applied for complete and partial defective sensors in an array antenna. The simulation results indicate that the proposed hybrid algorithm outperforms in terms of localization of failure sensor with a less number of measurements. It has been shown that the hybrid IRLS-SSF algorithm provides an accurate diagnosis of complete and partial defective sensors as compared to PCD, SSF or IRLS alone. Variety of simulations has been provided to validate the performance of the designed algorithms in diversified scenarios.

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“…In Ref. , array elements were specified to be wire dipoles which enabled mutual coupling to be accounted for by means of a readily calculated impedance matrix.…”

Section: Introductionmentioning

confidence: 99%

Response-correction-based fault detection in small linear microstrip patch arrays using magnitude-only far-field pattern samples

Koziel

Jacobs

2016

Int J RF and Microwave Comp Aid Eng

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We present a computationally efficient method for detecting faulty elements in a small linear microstrip patch array from samples of the array's far‐field magnitude radiation pattern (here represented by realistic EM simulations). Regardless of the array size, our method requires only one expensive full‐wave entire‐array simulation—compared to, e.g., the 696 required by the previous best method (Patnaik et al., IEEE Trans Antennas Propag 55 (2007), 775–777) for a 16‐element array. This one simulation gives the accurate far‐field magnitude pattern of the original defect‐free array, and is used in conjunction with the defect‐free array's analytical array factor to formulate a response correction function, which can then be used to construct an accurate approximation of the EM‐simulated pattern of any arbitrary faulty array at very low cost. The low cost and high accuracy of these approximations make possible an enumeration strategy for identifying the faulty elements, which would have been computationally prohibitive were EM‐simulated patterns to be used. Our method was robust in handling arrays of double the size considered in Patnaik et al., IEEE Trans Antennas Propag 55 (2007), 775–777, while expanding on (Patnaik et al., IEEE Trans Antennas Propag 55 (2007), 775–777) by also addressing partial faults and measurement noise. Accuracies in detecting up to three faults (including partial ones) in arrays of 16 and 32 elements exceeded 97% under noise‐free conditions, and were above 93% in the presence of 2 dB measurement noise. © 2016 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:683–689, 2016.

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Rapid Method for Finding Faulty Elements in Antenna Arrays Using Far Field Pattern Samples

Cited by 54 publications

References 14 publications

Detection and correction of errors in linear antenna arrays

Detection and correction of errors in linear antenna arrays

Diagnosis of Faulty Sensors in Phased Array Radar Using Compressed Sensing and Hybrid IRLS–SSF Algorithm

Response-correction-based fault detection in small linear microstrip patch arrays using magnitude-only far-field pattern samples

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