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. 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.

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.

Motivation

Drones have become popular in the modern world. They have many potential uses but also pose the following threats to society:

- Terrorist delivery device

- Collisions with commercial aircraft.

- Privacy concerns

Key selling points of ISAR over optical sensors:

- All weather capability

- Target and payload Identification

- Long range

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).

Approach

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.

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 an polar coordinate system, polar reformatting converts the data to a rectangular coordinate system.

Results

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 0.01m2 and 0.3m2, which means they scatter the same amount of energy as a small bird.

Drones backscatter vertically polarised signals more strongly than horizontally polarised signals.

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.

Conclusion

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 greater than this system achieved.

Future studies

MIMO setup

Investigate phase errors

ISAR images of flying drones