Machine Learning for Antenna Array Failure Analysis Lydia de Lange - PowerPoint PPT Presentation

Machine Learning for Antenna Array Failure Analysis Lydia de Lange Under Dr DJ Ludick and Dr TL Grobler Dept. Electrical and Electronic Engineering, Stellenbosch University MML 2019

Outline 15/03/2019 3

Introduction 15/03/2019 4

Antenna Arrays 5 15/03/2019

Reconstructed Sky Im Image 6 15/03/2019

Square Kilometer Array (S (SKA) 7 15/03/2019

Lydia’s Arrays (LA) and Far -Field Patterns 8 15/03/2019

Problem Statement Distorted results Inaccurate far-field (e.g. in Element failure patterns reconstructed sky (beam patterns) image) Important applications: • Array failure correction • System health management of large antenna arrays

Previous work Failed antenna element detection and location possible with machine learning techniques e.g.: • Feedforward neural networks • Support vector models 11 15/03/2019

Methodology 15/03/2019 12

Methodology Sampling Simulate scenarios Train methods for input data FNN

Sampling Methods 𝜒 ( ° ) 𝜄 ( ° ) Name Number of Samples Single cut ( 𝜒 = 0) 0 𝜄 ∈ [−90, 90] 181 Single cut ( 𝜒 = 45) 45 Single cut ( 𝜒 = 90) 90 Single cut ( 𝜒 = 135) 135 Principle cuts 0, 90 362 Diagonal cuts 45, 135 All cuts 724 0, 45, 90, 135 3-D pattern (182 samples) 182 3-D far-field pattern sampled in a ( 𝜄, 𝜒 ) grid. 3-D pattern (361 samples) 361 3-D pattern (725 samples) 725

Training of FNN Multi-label feedforward neural network 𝑦 Sampled far-field observation of 1 failure scenario 𝑧 y = ON or OFF state of each antenna in the array “multi - label” – 1 label for each antenna (25)

Adapt parameters 𝛾 with each pass until f is as Training of FNN similar as possible to true relationship.

Results 15/03/2019 22

Nature of FNN*: • ↑ Number of samples (S) • ↑ Number of parameters ( 𝛾 ) to be estimated • ↓ Accuracy • If accuracy ↑ : sampling pattern found a useful region in the 3-D far-field pattern to accurately identify failure scenarios * # training iterations = const. FNN Results 23 15/03/2019

FNN Results Dataset Samples Training Time Accuracy (%) (sec) 90ᵒ cut 181 31.98 69.70 3-D pattern 182 32.17 87.88 Diagonal cuts 362 35.48 90.91 All cuts 724 40.73 75.76

25 Additional experiments

Additional Experiments • Compared 14 other classification algorithms 1 according to accuracy using the 10 sampling method datasets. • Best 4: • FNN • One vs Rest Classifier + Linear SVC • One vs Rest Classifier + Logistic Regression • One vs Rest Classifier + Logistic Regression CV x 1 Scikit-learn algorithms

Classification Algorithm Comparison 100 Additional 90 Experiments 80 70 ACCURACY (%) 60 • Best: One vs Rest + 50 Logistic Regression CV 40 • 100% accuracy 30 achieved 20 • Number of parameters 10 vs accuracy relationship is different 0 • 3-D sampling method contains more SAMPLING METHOD DATASETS information than combined single cuts FNN OvR+LinearSVC OvR+LogisticRegression OvR+LogisticRegressionCV 27 15/03/2019

Conclusion 15/03/2019 28

Conclusion • FNN used to detect and locate failed antenna elements in a bow-tie antenna array • Investigated choice of training data on FNN accuracy and training time • Diagonal cuts – 90.91% accuracy, 35.48 secs • 3-D pattern (182 samples) – 87.88% accuracy, 32.17 secs • On larger datasets with more scenarios, the difference in training time may become more significant. • Additional work: • Best algorithm: One vs Rest + Logistic Regression CV • Best sampling method: 3-D pattern

Future work • Manufacturing and measuring an antenna array with a spherical nearfield scanner! • Look at SVMs • Looking at other places in pipeline to do ML on: Power Spectral Density and Correlations

Acknowledgement The financial assistance of the South African SKA project (SKA SA) towards this research is hereby acknowledged (www.ska.ac.za). 15/03/2019 31

• [1] R. J. Mailloux, “Array Failure Correction With A Digitally Beamformed Array,” IEEE Trans. Antennas Propag ., vol. 44, no. 12, pp. 1543 – 1550, 1996. • [2] P. Hall, “The Square Kilometre Array: An Engineering Perspective,” Springer , 2010. • [3] J. A. Rodrìguez, et al ., “A Comparison Among References Several Techniques For Finding Defective Elements In Antenna Arrays,” 2nd European Conference on Antennas and Propagation (EUCAP), pp. 1 – 8, 2007. • [4] I. Goodfellow, Y. Bengio, and A. Courville, “Deep Learning,” MIT Press , pp. 164 – 167, 2016. 15/03/2019