View source for Projects:2017s1-125 Drone Imaging and Classification using Radar

= Project Team =

Elliot Hansen

Pranjal Chowdhury

Zikai Liu

Ran Li

= Supervisors =

Dr Brian Ng

Dr Waddah Al-Ashwal



= Introduction =

Drones have entered the modern lexicon due to their widespread uses in commercial, research and military contexts. Their prevalence is expected to increase in the coming years, with safety and privacy implications for society. The Radar is a powerful technology that provides all-weather, day-to-day capabilities to detect and target.This project explores the use of radar to perform imaging of drones, and attempts to understand the characteristics in their radar returns. Inverse synthetic aperture radar (ISAR) images will be simulated, and a radar system will be used to make real measurements of consumer drones in the School’s anechoic chamber. The outcome of this project will provide a cornerstone for building effective radar systems capable of surveillance and identification of drones.

'''Radar Fundamentals''' 
Radar works by using electromagnetic (EM) waves to illuminate objects in the environment and receives reflected echoes by the objects. From the received signals, the position of the targets can be measured by calculating the time taken to receive the transmitted signal. The velocity of the target can be measured by measuring the Doppler shift between the transmitted and received signal.

'''Inverse Synthetic Aperture Radar (ISAR)''' 
ISAR is a powerful signal-processing tool for generating 2D radar images by examining the Doppler histories of a target from its rotational movement. ISAR works using the principle that rotation of the target is equivalent to viewing the target over a circular orbit, which increase the aperture the target is viewed over. Increased aperture is directly related to an increase in resolution achievable.

'''Significance''' 
Regular pulse Doppler radar cannot easily identify drones, as they appear as clutter. Pulse Doppler radar struggles to detect drones from birds due to similar sizes. This project aims to find a way to detect these drones. Radar is attractive when compared to optical sensors due to its long range, all time and weather capability. Inverse synthetic aperture radar is a method which forms 2D images that can classify targets and could help governments enforce drone regulations. 

'''Objectives'''
 -Gain knowledge about the application of ISAR. 
 -Build an ISAR system capable of producing imagery of consumer drones. 
 -Form ISAR images of a consumer drone in an anechoic chamber.  
 -Analyse the images and understand the characteristics of the drones' radar returns.

= Aim and Motivation =

The aim of this project to build a radar system to understand fundamental radar concepts and to explore the application of ISAR imaging in the real world for target identification by generating images of a consumer drone. Since the Consumer drones have become prevalent in our society with millions being sold each year. Drones are everywhere, anybody can buy and use them. Also, Drones have made a significant impact on the modern world. Therefore, they are a serious problem for society. They have many potential uses but also pose the following threats to society, such as Terrorist threat, for example,Illegal use of drones, flying drones near airports can put lives at risk.  There is a danger that terrorists can attack using drones with bomb in crowded areas. Privacy, for example like spying on people which is illegal. Drones also create problems regarding security and privacy for governments, industry and citizens. As the Regular pulse Doppler radar cannot easily identify drones as they appear as clutter. ISAR is the one potential solution to avoid this problems.

= Theory =

ISAR exploits the rotational motion of a target to form a synthetic aperture, which increases the resolution of the images which can be formed by increasing the directivity of the antennas. A high resolution image requires a fine range resolution (Δ𝑟_𝑟=𝑐/2𝐵), requiring a large signal bandwidth (B) and a fine cross range resolution (Δ𝑟_𝑐𝑟=𝜆/2Ω), requiring a large aspect angle the target is viewed over (Ω). ISAR generates 2D images by examining the Doppler histories of a target from its movement, to separate scatterers in the cross range dimension. ISAR images cause 3D objects to be projected on a 2D imaging plane. This plane is motion dependent, therefore non-cooperative targets can form distorted images.

= Approach =
[[File:Image processing block diagram.jpg|Image processing block diagram|600px]]

A National Instruments Vector Signal Transceiver was used to transmit and receive a stepped frequency chirp waveform, to provide 2GHz of signal bandwidth. A drone was mounted on a turntable and backscattered signals were recorded at a range of different aspect angles inside an anechoic chamber. The turntable was used to apply the necessary rotational motion for ISAR imaging.

[[File:Bdrone_chamber.JPG|Large drone in chamber|600px]]


ISAR image formation involves generating a range profile using a matched filter, then taking a Fourier transform (FFT) over Doppler to form the final image. A prominent stationary clutter source was used to apply a phase correction to the data. ISAR images are naturally captured in a polar coordinate system, polar reformatting converts the data to a rectangular coordinate system.

Check out this video for a montage showing how we conducted the experiment. [https://www.youtube.com/watch?v=o8lyJY24xyc Video]

= Results =

[[File:Isar_507_z_2_edited.JPG|ISAR image of large drone|600px|]]

The ISAR image was generated using the radar configuration shown in the Table alongside. A scaled outline of the drone has been superimposed onto the image. Forty separate range profiles were used to generate the image. The central hub of the drone provided the strongest returns. The arms of the drone provided weaker returns. The propellers are not visible on the image.



= Discussion =

The drones were found to have an radar cross section of between 0.01m^2 and 0.3m^2, which means they scatter the same amount of energy as a small bird. The vertically polarised images were superior for detection and classification, due to excessive noise in the horizontal polarised images. This may have been an artefact of the experimental set up.

Zero Doppler clutter reduction and phase correction autofocus algorithms were critical for developing focused imagery. Practical challenges such as synchronised timing difficulties, phase errors, coherency difficulties and clutter within the chamber, reduced the quality of the images formed.

To improve a high quality ISAR image, zero doppler clutter reduction and phase correction autofocus algorithms were critical for developing focused imagery. Clutter limited, signal processing method that can possible reduce the clutter and therefore improve the system. Also, the polar reformatting can be used for image formation to enhance image quality.

= Conclusion =

An ISAR system was designed and built. ISAR images of two drones were successfully formed. The radar system was found to be clutter limited. A commercial ISAR system for drone image should use resolutions finer than this system provided.

'''Future studies'''
  - MIMO setup
  - Investigate phase errors
  - ISAR images of flying drones

= Reference =
[1] Victor C. Chen and Marco Martorella 2014, Inverse Synthetic Aperture Radar Imaging Principles, Algorithms and Applications, SciTech Publishing

[2] Li, C. J. & Ling, H. An Investigation on the Radar Signatures of Small Consumer Drones IEEE Antennas and Wireless Propagation Letters, Institute of Electrical and Electronics Engineers (IEEE), 2016

[3] Ritchie, M.; Fioranelli, F.; Griffiths, H. & Torvik, B. Micro-drone RCS analysis 2015 IEEE Radar Conference, Institute of Electrical and Electronics Engineers (IEEE), 2015

[4] Radar Basics, Stepped frequency chirp, accessed on 26/03/2017, URL: http://www.radartutorial.eu/02.basics/Stepped%20Chirp%20Radar.en.html 

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[10] Kerr, D, “Polar reformatting for ISAR imageing,” IEEE 1998 National Radar Conference, 12-13 May 1998

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[19]. Francesco Prodi, “ISAR cross-range scaling using a correlation based functional”, IEEE (2008).