Radar UAV and Bird Signature comparisons with Micro-Doppler

This chapter reviews the similarities and differences between micro Unmanned Aerial Vehicles (UAVs), also referred to as drones, and bird targets from the signals they present to radar sensors. With the increasing usage of UAV platforms in both military and civilian applications, the demand for the ability to sense drone locations and discriminate them from background clutter and non-drone targets is becoming a vital requirement. A comparable target in size, speed and Radar Cross Section (RCS) is a bird. These are present almost everywhere that radar systems have to operate and have been detected by radar since the early origin of radar engineering. Due to the similarity in radar signature birds can cause common misclassification between them and the priority drone targets which has been identified as a current key challenge in radar sensing. In this chapter radar bird and drone signature research is initially summarised, then a fundamental model that represents the key contributions from drone rotor blades is introduced and compared to real measurements. Laboratory measurements of quadcopter rotor blade signatures with across 4 linear polarisations are then investigated in order to evaluate the trend of Signal-to-Noise-Ratio (SNR) vs. aspect angle. Next bird signatures from two separate radar systems are shown and compared to drone targets also present in the captures which are of comparable size and RCS. The outputs of all research presented are then summarised in the concluding remarks

Classification of Targets Using Statistical Features from Range FFT of mmWave FMCW Radars

publishedVersio

Radar Detection, Tracking and Identification for UAV Sense and Avoid Applications

Advances in Unmanned Aerial Vehicle (UAV) technology have enabled wider access for the general public leading to more stringent flight regulations, such as the line of sight restriction, for hobbyists and commercial applications. Improving sensor technology for Sense And Avoid (SAA) systems is currently a major research area in the unmanned vehicle community. This thesis overviews efforts made to advance intelligent algorithms used to detect, track, and identify commercial UAV targets by enabling rapid prototyping of novel radar techniques such as micro-Doppler radar target identification or cognitive radar. To enable empirical radar signal processing evaluations, an S-Band and X-Band frequency modulated, software-defined radar testbed is designed, implemented, and evaluated with field measurements. The final evaluations provide proof of functionality, performance measurements, and limitations of this testbed and future software-defined radars. The testbed is comprised of open-source software and hardware meant to accelerate the development of a reliable, repeatable, and scalable SAA system for the wide range of new and existing UAVs

A three-step classification framework to handle complex data distribution for radar UAV detection

Unmanned aerial vehicles (UAVs) have been used in a wide range of applications and become an increasingly important radar target. To better model radar data and to tackle the curse of dimensionality, a three-step classification framework is proposed for UAV detection. First we propose to utilize the greedy subspace clustering to handle potential outliers and the complex sample distribution of radar data. Parameters of the resulting multi-Gaussian model, especially the covariance matrices, could not be reliably estimated due to insufficient training samples and the high dimensionality. Thus, in the second step, a multi-Gaussian subspace reliability analysis is proposed to handle the unreliable feature dimensions of these covariance matrices. To address the challenges of classifying samples using the complex multi-Gaussian model and to fuse the distances of a sample to different clusters at different dimensionalities, a subspace-fusion scheme is proposed in the third step. The proposed approach is validated on a large benchmark dataset, which significantly outperforms the state-of-the-art approaches

Signal fingerprinting and machine learning framework for UAV detection and identification.

Advancement in technology has led to creative and innovative inventions. One such invention includes unmanned aerial vehicles (UAVs). UAVs (also known as drones) are now an intrinsic part of our society because their application is becoming ubiquitous in every industry ranging from transportation and logistics to environmental monitoring among others. With the numerous benign applications of UAVs, their emergence has added a new dimension to privacy and security issues. There are little or no strict regulations on the people that can purchase or own a UAV. For this reason, nefarious actors can take advantage of these aircraft to intrude into restricted or private areas. A UAV detection and identification system is one of the ways of detecting and identifying the presence of a UAV in an area. UAV detection and identification systems employ different sensing techniques such as radio frequency (RF) signals, video, sounds, and thermal imaging for detecting an intruding UAV. Because of the passive nature (stealth) of RF sensing techniques, the ability to exploit RF sensing for identification of UAV flight mode (i.e., flying, hovering, videoing, etc.), and the capability to detect a UAV at beyond visual line-of-sight (BVLOS) or marginal line-of-sight makes RF sensing techniques promising for UAV detection and identification. More so, there is constant communication between a UAV and its ground station (i.e., flight controller). The RF signals emitting from a UAV or UAV flight controller can be exploited for UAV detection and identification. Hence, in this work, an RF-based UAV detection and identification system is proposed and investigated. In RF signal fingerprinting research, the transient and steady state of the RF signals can be used to extract a unique signature. The first part of this work is to use two different wavelet analytic transforms (i.e., continuous wavelet transform and wavelet scattering transform) to investigate and analyze the characteristics or impacts of using either state for UAV detection and identification. Coefficient-based and image-based signatures are proposed for each of the wavelet analysis transforms to detect and identify a UAV. One of the challenges of using RF sensing is that a UAV\u27s communication links operate at the industrial, scientific, and medical (ISM) band. Several devices such as Bluetooth and WiFi operate at the ISM band as well, so discriminating UAVs from other ISM devices is not a trivial task. A semi-supervised anomaly detection approach is explored and proposed in this research to differentiate UAVs from Bluetooth and WiFi devices. Both time-frequency analytical approaches and unsupervised deep neural network techniques (i.e., denoising autoencoder) are used differently for feature extraction. Finally, a hierarchical classification framework for UAV identification is proposed for the identification of the type of unmanned aerial system signal (UAV or UAV controller signal), the UAV model, and the operational mode of the UAV. This is a shift from a flat classification approach. The hierarchical learning approach provides a level-by-level classification that can be useful for identifying an intruding UAV. The proposed frameworks described here can be extended to the detection of rogue RF devices in an environment

Development of a K-band FMCW flexible radar prototype for detection and classification of nano-drones

© Cranfield University 2020. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright ownerNano-drones of the size of an insect can be used to perform stealthy surveillance or to gather intelligence crucial to attack roles at a relatively short range and within enclosed spaces and buildings. Conventional radar systems have been optimised to detect and classify bigger targets and are not specifically designed to detect nano-targets of less than 5 cm in size. Hence, this project aims to develop a radar system to detect and classify an insect-like size drone that corresponds to a low RCS. This will exhibit challenges due to the nature of the weak echoed signal that will be masked by an uninterested target with a stronger echoed signal. To tackle this sort of problem, micro Doppler extraction is applied for better target detection. This type of target that consists of a bladed propeller will give rise to a significant micro-Doppler signature that will contribute to the discernment of the interested target. An ad-hoc S-band FMCW radar prototype using off-the-shelf components An ad-hoc S-band FMCW radar prototype using off-the-shelf components has been successfully delivered. This prototype act as a groundwork for the next research phase of design and development for a higher frequency. Then, with the strong foundation of the S-band demonstrator, a flexible K-band FMCW radar prototype has successfully delivered aiming to meet the research purpose. The radar prototype offers a wide range of flexibility for the user to select the radar parameters (like operating frequency, ramp duration, bandwidth and integration time) and configure its performance. It will collect the signatures of real targets (nano-drone model) so that their performance can be assessed on experimental data. The results demonstrated that a nano-drone, a small size of less than 5 cm can be detected with the radar prototype developed.Ph