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Deep learning cognitive radar for Micro UAS

detection and classification

Gihan J. Mendis

Dept. of Computer and Electrical Engineering

The University of Akron

Akron, USA

ijm11@zips.uakron.edu

Jin Wei

Dept. of Computer and Electrical Engineering

The University of Akron

Akron, USA

jwei1@uakron.edu

Arjuna Madanayake

Dept. of Computer and Electrical Engineering

The University of Akron

Akron, USA

arjuna@uakron.edu

Abstract— In this paper, we propose an intelligent cognitive

radar system for detecting and classifying the micro unmanned

aerial systems (micro UASs). In this system, we design a low-

complexity binarized deep belief network (DBN) classifier that

recognizes the signature patterns generated by using a Doppler

radar based solution. To generate the distinguishable patterns, our

work employs the spectral correlation function (SCF) that is noise

resilient. In the experiment conducted, micro UASs are clamped

to be immobile while propellers are on motion. Doppler effects

caused by propeller motions of UASs are considered. By

employing our binarized DBN, the computationally costly 91600

floating point multiplication operations required in the original

DBN are represented by using zero computational cost no

connections, simple connections, negation operations, bit-shifting

operations, and bit-shifting with negation operations. In the

simulation section, we show that the proposed system gives more

than 90% accuracy in detecting the micro UASs in the

environments with SNR ≥ -3 dB AWGN noise. Furthermore, the

classification accuracy of different micro UASs remains more than

90% for environments with SNR ≥ 0 dB AWGN noise.

Keywords—micro UAS; drones; Doppler radar; spectral

correlation function; deep belief network, low complexity; deep

learning

I. INTRODUCTION

Unmanned aerial systems (UASs) are common as

surveillance devices in the military. Micro UASs are less

expensive UASs, which are smaller in dimensions and weight.

They are also referred as “drones” and are useful for a variety of

civilian applications including agriculture, package delivery,

wildlife monitoring, leisure activities etc. However, the small

size and the slow moving speed of micro UASs make them

undetectable from regular radar systems design to detect larger

and fast moving aerial systems. Wide availability and

undetectable nature of micro UASs introduce a new security

thread [1].

Some radar based systems have been developed recently to

detect and classify micro UASs. In [2] Shin et al., a K-band radar

system with fiber-optic links for detect micro UASs is

introduced. In [3] Drozdowicz et al. presented an experimental

system for the detection and tracking of micro UASs. In [4]

Jahangir et al. used 2-D L-Band receiver arrays to detect micro

UASs and machine learning decision tree classifier was used to

reject other targets. In our previous work, we proposed a

Doppler radar based method for detecting micro UASs [5-6].

Deep learning is an emerging area of machine learning that

empower recent achievements in machine intelligent. Deep

learning methods are artificial neural network (ANN) based

machine learning techniques with multiple layers of ANNs.

Deep learning methods are capable of learning suitable features

from raw data. Therefore, they are more effective in completing

complex tasks [7]. Deep learning methods have been used for

pattern recognition applications in various areas including

speech recognition, natural language processing, audio and

music processing, image recognition, and machine vision [8-

16].

In our previous work, we used a low cost 2.4 GHz

continuous-wave Doppler radar system. This system is built

using commercially available RF components alone with a

signal processing mechanism that use spectral correlation

function (SCF) to generated noise-resilient and distinguishable

2-D signature patterns and robust deep belief network (DBN)

deep learning method as the SCF signature pattern classification

method. The radar system was set up in a laboratory

environment, data were collected for 3-micro UASs, and

collected data were used to verify the signal processing

mechanism.

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