Classification of drones and birds using convolutional neural networks applied to radar micro-Doppler spectrogram images

Funding: UK Science and Technology Facilities Council ST/N006569/1 (DR).This study presents a convolutional neural network (CNN) based drone classification method. The primary criterion for a high-fidelity neural network based classification is a real dataset of large size and diversity for training. The first goal of the study was to create a large database of micro-Doppler spectrogram images of in-flight drones and birds. Two separate datasets with the same images have been created, one with RGB images and other with grayscale images. The RGB dataset was used for GoogLeNet architecture-based training. The grayscale dataset was used for training with a series architecture developed during this study. Each dataset was further divided into two categories, one with four classes (drone, bird, clutter and noise) and the other with two classes (drone and non-drone). During training, 20% of the dataset has been used as a validation set. After the completion of training, the models were tested with previously unseen and unlabelled sets of data. The validation and testing accuracy for the developed series network have been found to be 99.6% and 94.4% respectively for four classes and 99.3% and 98.3% respectively for two classes. The GoogLenet based model showed both validation and testing accuracies to be around 99% for all the cases.PostprintPeer reviewe

Multiple drone classification using millimeter-wave CW radar micro-Doppler data

Funding: Army Research Laboratory under Cooperative Agreement Number: W911NF-19-2-0075.This paper investigates the prospect of classifying different types of rotary wing drones using radar. The proposed method is based on the hypothesis that the rotor blades of different sizes and shapes will exhibit distinct Doppler features. When sampled unambiguously, these features can be properly extracted and then can be used for classification. We investigate various continuous wave (CW) spectrogram features of different drones obtained with a low phase noise, coherent radar operating at 94 GHz. Two quadcopters of different sizes (DJI Phantom Standard 3 and Joyance JT5L-404) and a hexacopter (DJI S900) have been used during the experimental trial for data collection. For classification training, we first show the limitation of the feature extraction based method. We then propose a convolutional neural network (CNN) based approach in which the classification training is done by using micro-Doppler spectrogram images. We have created an extensive dataset of spectrogram images for classification training, which have been fed to the existing GoogLeNet model. The trained model then has been tested with unseen and unlabelled data for performance verification. Validation accuracy of above 99% is achieved along with very accurate testing results, demonstrating the potential of using neural networks for multiple drone classification.Postprin

Personnel recognition and gait classification based on multistatic micro-doppler signatures using deep convolutional neural networks

In this letter, we propose two methods for personnel recognition and gait classification using deep convolutional neural networks (DCNNs) based on multistatic radar micro-Doppler signatures. Previous DCNN-based schemes have mainly focused on monostatic scenarios, whereas directional diversity offered by multistatic radar is exploited in this letter to improve classification accuracy. We first propose the voted monostatic DCNN (VMo-DCNN) method, which trains DCNNs on each receiver node separately and fuses the results by binary voting. By merging the fusion step into the network architecture, we further propose the multistatic DCNN (Mul-DCNN) method, which performs slightly better than VMo-DCNN. These methods are validated on real data measured with a 2.4-GHz multistatic radar system. Experimental results show that the Mul-DCNN achieves over 99% accuracy in armed/unarmed gait classification using only 20% training data and similar performance in two-class personnel recognition using 50% training data, which are higher than the accuracy obtained by performing DCNN on a single radar node

The Use of Low-Cost Sensors and a Convolutional Neural Network to Detect and Classify Mini-Drones

The increasing commercial availability of mini-drones and quadrotors has led to their greater usage, highlighting the need for detection and classification systems to ensure safe operation. Instances of drones causing serious complications since 2019 alone include shutting down airports [1-2], spying on individuals [3-4], and smuggling drugs and prohibited items across borders and into prisons [5-6]. Some regulatory measures have been taken, such as registration of drones above a specific size and the establishment of no-fly zones in sensitive areas such as airports, military bases, and national parks. While commercial systems exist to detect drones [7-8], they are expensive, unreliable, and often rely on a single sensor. This thesis will explore the practicality of using low-cost, Commercial-off-the-shelf (COTS) sensors and machine learning to detect and classify drones. A Red, Green, and Blue (RGB) USB camera [9], FLIR Lepton 3.0 thermal camera [10], miniDSP UMA-16 acoustic microphone array [11], and a Garmin LIDAR [12] were mounted on a robotic sensor platform and integrated using a Minisforum Z83-F with 4GB RAM and Intel Atom x5-Z8350 CPU to collect data from drones operating in unstructured, outdoor, and real-world environments. Approximately 1,000 unique measurements were taken from three mini-drones – Parrot Swing, Parrot Quadcopter, and Tello Quadcopter – using the RGB, thermal, and acoustic sensors. Deep Convolutional Neural Network (CNNs), based on Resnet-50 [13-14], trained to classify the drones, achieved accuracies of 96.6% using the RGB images, 82.9% using the thermal images, and 71.3% using the passive acoustic microphone array

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

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Review of radar classification and RCS characterisation techniques for small UAVs or drones

This review explores radar-based techniques currently utilised in the literature to monitor small unmanned aerial vehicle (UAV) or drones; several challenges have arisen due to their rapid emergence and commercialisation within the mass market. The potential security threats posed by these systems are collectively presented and the legal issues surrounding their successful integration are briefly outlined. Key difficulties involved in the identification and hence tracking of these `radar elusive' systems are discussed, along with how research efforts relating to drone detection, classification and radar cross section (RCS) characterisation are being directed in order to address this emerging challenge. Such methods are thoroughly analysed and critiqued; finally, an overall picture of the field in its current state is painted, alongside scope for future work over a broad spectrum