Application of Machine Learning to Antenna

Design and Radar Signal Processing: A Review

Youngwook Kim Electrical and Computer Engineering, California State University, Fresno, 2320 E. San Ramon Ave, Fresno, CA93740

Abstract — Recently, machine learning has received a great

deal of attention thanks to its success in a range of applications

in engineering science, medicine, and economics. The new technology has also been gaining popularity in electromagnetics. In this paper, the application of machine learning algorithms,

for antenna design and radar signal processing, is reviewed.

Index Terms — Machine learning, Deep learning, Antenna optimization, Radar signal processing, Deep convolution neural

networks, Transfer learning, Classification.

1. Introduction

Machine learning is one of the rapidly emerging

disciplines that can be widely applied in the fields of

engineering, science, medicine, and economics, to name just

a few. The area is a subset of artificial intelligence that uses

computational statistics to find a mathematical model

describing input and output data. The constructed

mathematical model, referred to as a data-driven archetype,

can offer a substitute for an analytical counterpart. Because

the data-driven model can interpolate output based on

unknown input, it can solve regression problems. With

classification problems sharing the same fundamental basis

as regression, machine learning has been used intensively for

classification problems such as image recognition, speech

recognition, and target classification [1].

Recently, the application of machine learning has also

been extended to electromagnetics (EM). Thanks to the

advancement of machine learning algorithms, especially

deep-learning technology that enables the modeling of high-

level abstractions in data, many antenna parameter

optimization and radar target classification problems could

be revisited. Antenna optimization, which requires high

computational complexity, can be addressed by machine

learning algorithms to reduce the time cost involved. Rather

than employing a computationally expensive EM simulator,

machine learning, such as that found in artificial neural

networks, can substitute the EM simulator following training

[2]. In addition, target classification, based on radar

measurement, has been a significant topic of machine

learning for defense and surveillance purposes for decades

[3–5].

In this paper, we review the applications of machine

learning in the EM field, in particular antenna design and

radar target classification. The basic concepts of machine

learning and deep learning are discussed, and their

applications for antenna geometric parameter optimization

and target classification, based on radar imagery, are

addressed.

2. Machine Learning Algorithms

When the analytical model of a system is not available,

while input and output data can be measured or simulated,

machine learning can be an alternate tool to explain the

system via mathematical equations. The mathematical model

includes numerous parameters that can be optimized to

approximate the system transfer function. The determination

of the parameter is referred to as a training process that

searches for parameters that make the model best fit the data.

Among machine learning algorithms, the neural network

is one of the most famous mathematical structures to have

become a foundation of the data-driven model [6]. The

neural network consists of several layers, each of which is

composed of several perceptrons. The perceptrons of one

layer are connected with those of another layer via weighting

factors. In the perceptron, a non-linear function, known as

the activation function, is embedded to enable the nonlinear

description of the system. The structure of the neural

network is shown below.

Fig. 1. Structure of neural networks.

Conventionally, a single inner layer has been used because

this has been proven sufficient to approximate any non-linear

systems. In addition, multiple layers induce more parameters

to be found, meaning that the computational complexity and

limited memory size are the main restrictions on its

feasibility.

3. Deep Learning

The difference between general machine learning and deep

learning is that the latter uses multiple layers to enable more

powerful abstractions and generalization. The conventional

use of neural networks requires a prior feature extraction

process to reduce the networks’ computational burden. If the

features need to be extracted inside the neural networks, they

will require a large network size with powerful training

capability. Thus, the chance of successful training was not

2018 International Symposium on Antennas and Propagation (ISAP 2018)October 23~26, 2018 / Paradise Hotel Busan, Busan, Korea

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high, considering a practical computational power and

memory size. Recently, advances in computer power and

larger memory sizes have enabled the successful training of

large networks. In deep learning, feature extraction and

classification/generalization can be performed in a single

network, leading to remarkable improvement. Various deep

learning technologies have seen success, including deep

neural networks, deep convolutional neural networks

(DCNN), and deep recursive neural networks (DRNN), to

name a few. In particular, DCNN has obtained great

popularity in image classification, while DRNN has shown

its capability in time-domain signal processing.

4. Application to Antenna Design

Because antenna design is involved with a

computationally expensive EM simulator, the simulation

time ranges from a few seconds to a few days, depending on

the size of the antenna, operating frequency, and

computational power. In particular, because the antenna

needs to be optimized to maximize the performance while

reducing the size, rapid optimization techniques are highly

desirable. Rather than using an EM simulator, machine

learning algorithms can work as an alternative to the general

optimization techniques such as genetic algorithm, particle

swarm optimization, and simulated annealing. For example,

the relationship between the size of the patch antenna and its

resonant frequency is trained by neural networks, allowing

the size to be immediately determined through the trained

machine [2]. Another approach is to estimate the parameter

for the model of the antenna parameter using the machine

learning technique. The antenna characteristics, which can be

expressed by a mathematical model, can be determined if the

model parameters are known [7]. Array antenna designs have

also been addressed by neural networks [8].

5. Application to Radar Signal Processing

One of the purposes of radar signal processing is to

identify and recognize the measured target. The latter’s

features, such as RCS, range profile, and radar imagery, offer

a possibility of target recognition. Conventionally, the

classifiers based on machine learning algorithms, such as

neural networks and support vector machines, have been

used for target recognition problems. In particular, DCNN

has recently garnered significant attention in image

classification problems, with its success encouraging its use

for radar imagery. Convolutional filters extract features and

the fully connected layers construct a class boundary in a

single neural network. As such, DCNN has been applied to

micro-Doppler signatures. Human activities, hand gestures,

aquatic activities, drones, and vehicles are measured using

radar, and the corresponding spectrogram is classified using

DCNN. Another notable advance was made in synthetic

aperture radar (SAR) images for automatic target recognition.

This not only allowed the SAR image classification to be

improved, the whole process can now be handled by DCNN

rather than the traditional routine of detection, discrimination,

and classification.

One of the bottlenecks of using deep learning for radar

imagery is the lack of training data. Unlike with optical

camera images, high costs are involved in obtaining the radar

images, meaning that the number of images is not sufficient

to train large networks. To overcome this issue, transfer

learning has been suggested, which has shown great

improvement [4]. By borrowing the pre-trained

convolutional neural networks, such as AlexNet and VGG16,

only the last stage, the fully connected layer, needs to be re-

trained.

(a)

(b)

Fig. 2. (a) DCNN for micro-Doppler processing and (b) DCNN for SAR classification [9].

6. Conclusion

In this paper, we briefly reviewed the applications of deep

learning to the applied EM. Greater advances in machine

learning algorithms would benefit other areas, including EM.

Although a distinct improvement could have been found in

the radar image classification, it is expected that its use could

augment the antenna design area, such as helping in the

design of conventional antennae by non-experienced

engineers or identifying new antenna structures.

References

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Statistical Learning (with applications in R), Springer, 2013.

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[7] Y. Kim, S. Keely, J. Ghosh, and H. Ling, “Application of artificial neural networks to broadband antenna design based on a parametric

frequency model,” IEEE Transactions on Antennas and Propagation, vol. 55, pp. 669–674, Mar. 2007.

[8] A. Rawata, R. N. Yadavb, and S. C. Shrivastavac, “Neural network

applications in smart antenna arrays: A review,” International Journal of Electronics and Communications, Vol. 66, May 2012.

[9] C. Danilla, “Convolutional neural networks for contextual de-noising and classification of SAR images,” M.S. Thesis, University of

Twente, 2017.

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