Active Antenna Array Diagnosis from Far-Field Measurements

Famoriji, Oluwole John; Zhang, Zhongxiang; Fadamiro, Akinwale O.; Khan, Zakir; Lin, Fujiang

doi:10.1109/cicta.2018.8706071

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Conversely, if α j parameters are determined via the active element patterns [22], then the impacts of the mutual coupling are considered in full as against the N full wave EM simulations for each measurement point. There are more methods in literature that include AEAP [23] and PLCM methods [24,25] (reference [26] is recommended for quality review). Since the mutual coupling model is changeable based on the computational resource that is available, the measured field can be mathematically expressed as [9]…”

Section: Mutual Coupling Modelingmentioning

confidence: 99%

Antenna Array Fault Diagnosis in the Presence of Mutual Coupling Using Excitations Optimization Technique: A Case Study of Rectangular Waveguide Array

Famoriji¹,

Shongwe²

2022

PIER C

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Fault diagnosis in antenna array remains a key research topic, because it affects the symmetry and consequently causes deterioration in the expected radiation pattern. There are many compressive sensing based array diagnosis methods that have been developed; however, they depend on simple array factor formulation, and ignore mutual coupling impacts that exist among the elements. In this paper, it is demonstrated how this assumption causes poor diagnosis when there is weighty mutual coupling via two general models: average embedded antenna pattern (AEAP) and port-level coupling matrix (PLCM). In addition, the optimization of antenna excitations is conducted to mitigate the mutual coherence of measurement matrix of the system, consequently lead to a smaller measurements needed to successfully detect faults. Results from numerical experiment show that the consideration of mutual coupling impact provides a reliable and effective fault diagnosis. Finally, the proposed scheme is implemented via one measurement probe in the space, which indicates its potential practical application and deployment in antenna array systems.

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Section: Mutual Coupling Modelingmentioning

confidence: 99%

Antenna Array Fault Diagnosis in the Presence of Mutual Coupling Using Excitations Optimization Technique: A Case Study of Rectangular Waveguide Array

Famoriji¹,

Shongwe²

2022

PIER C

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“…As a result, there will always be a demand for fast and accurate complex antenna systems diagnosis, to resolve the unacceptable radiation pattern distortion caused by element (s) failure in the array (Figure .1). Many antenna array diagnosis methods, based on genetic algorithms [1], [2], exhaustive search [3], matrix inversion [4], and MUSIC [5], have been developed in literature to identify faulty antenna elements in an array. All the methods compare the array under test (AUT) with the radiation pattern of an "error free" reference array.…”

Section: Introductionmentioning

confidence: 99%

“…All the methods compare the array under test (AUT) with the radiation pattern of an "error free" reference array. All the methods in [1][2][3][4][5] need big measurement samples for large antenna arrays, to get reliable diagnosis. Reducing the measurement samples, compressive sensing (CS) based techniques have been reported in [6]- [9].…”

Section: Introductionmentioning

confidence: 99%

“…(c) they do not perform optimization on CS measurement matrices. The shortcomings of CS based methods [6]- [10], and the high time of diagnosis required by methods in [1]- [5] and complexity in measurement time cost motivate the need for an intelligent antenna array diagnosis method. Inspired and motivated by [11], an approach based on artificial neural network is presented to model the inverse radiation problem for automated and simple antenna array diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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An Effective Antenna Array Diagnosis Method via Multivalued Neural Network Inverse Modeling Approach

Famoriji

Shongwe

2021

AEM

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Failure of element (s) in antenna arrays impair (s) symmetry and lead to unwanted distorted radiation pattern. The replacement of defective elements in aircraft antennas is a solution to the problem, but it remains a critical problem in space stations. In this paper, an antenna array diagnosis technique based on multivalued neural network (mNN) inverse modeling is proposed. Since inverse analytical input-to-output formulation is generally a challenging and important task in solving the inverse problem of array diagnosis, ANN is a compelling alternative, because it is trainable and learns from data in inverse modelling. The mNN technique proposed is an inverse modelling technique, which accommodates measurements for output model. This network takes radiation pattern samples with faults and matches it to the corresponding position or location of the faulty elements in that antenna array. In addition, we develop a new training error function, which focuses on the matching of each training sample by a value of our proposed inverse model, while the remaining values are free, and trained to match distorted radiation patterns. Thereby, mNN learns all training data by redirecting the faulty elements patterns into various values of the inverse model. Therefore, mNN is able to perform accurate array diagnosis in an automated and simpler manner.

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A survey on improving the wireless communication with adaptive antenna selection by intelligent method

Lai

2022

Computer Communications

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Active Antenna Array Diagnosis from Far-Field Measurements

Cited by 5 publications

References 4 publications

Antenna Array Fault Diagnosis in the Presence of Mutual Coupling Using Excitations Optimization Technique: A Case Study of Rectangular Waveguide Array

Antenna Array Fault Diagnosis in the Presence of Mutual Coupling Using Excitations Optimization Technique: A Case Study of Rectangular Waveguide Array

An Effective Antenna Array Diagnosis Method via Multivalued Neural Network Inverse Modeling Approach

A survey on improving the wireless communication with adaptive antenna selection by intelligent method

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