DIAT-RadSATNet—A Novel Lightweight DCNN Architecture for Micro-Doppler-Based Small Unmanned Aerial Vehicle (SUAV) Targets’ Detection and Classification

Kumawat, Harish Chandra; Chakraborty, Mainak; Raj, A. Arockia Bazil

doi:10.1109/tim.2022.3188050

Cited by 34 publications

(13 citation statements)

References 36 publications

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“…CNNs are also applied for the classification of drones or their motion states based on micro-Doppler signatures in, e.g., [40], [41]. For detection and classification, a deep CNN is applied in [42] and a long short-term memory neural network for detection, classification, and localization is utilized in [43], in which they handle the localization by determining the angle for the received micro-Doppler pattern over time. The work [44] suggests a model which processes radar data using convolutional layers in a variational autoencoder to learn features to be used for different applications like estimating the distance to a corner reflector.…”

Section: B Related Workmentioning

confidence: 99%

Indoor Localization based on Short-Range Radar and Rotating Landmarks

Thormann,

Steuernagel,

Baum

2024

Preprint

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A novel concept for indoor self-localization based on relative position measurements to rotating artificial landmarks (with known positions) using short-range radar is proposed. This includes a complete processing pipeline for extracting distance and angle measurements from the raw radar data, which consists of a neural network for distance estimation, a basic angle-of-arrival estimator, and a particle filter for position tracking. Due to the ability of radar to measure range rate, i.e., the velocity in the direction of a detection, it is possible to robustly detect the landmarks by detecting and localizing their micro-Doppler pattern. This mean localization is possible even under difficult conditions (e.g., light changes). Experiments with a wheeled mobile robot and common office fans as landmarks demonstrate the effectiveness of the approach for indoor localization.

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Section: B Related Workmentioning

confidence: 99%

Indoor Localization based on Short-Range Radar and Rotating Landmarks

Thormann,

Steuernagel,

Baum

2024

Preprint

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“…Moreover, the dataset information of current research on radar-based methods using ML for UAV detection and classification is shown in Table 9. However, many works have been conducted based on ML techniques for the detection of UAVs using radar technology [146][147][148][149][150][151][152][153][154][155]. A non-cooperative UAV monitoring technique proposed in [146] utilized decision tree (DT) and SVM classifiers, along with the inclusion of MDS for UAV detection and classification.…”

Section: Uav Classification Based On ML Using Radarmentioning

confidence: 99%

“…10000 complex-valued Gaussian noise signals of the same period t were included in each dataset, along with 2000 time series (samples) of each of the 5 drone classes. [149] A dataset named "DIAT-µ SAT" collected m-D signature T-F image samples, and here the total number of spectrogram images was [6 × 4849]. More details of datasets can be found in the study in [176].…”

Section: Uav Classification Based On ML Using Radarmentioning

confidence: 99%

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Rahman,

Sejan,

Aziz

et al. 2024

Remote Sensing

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Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches.

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“…Authors in [26] proposed DIAT-RadSATNet containing modules from SqueezeNet and MobileNet for multi-class classification. They summarized the effects of different dimension filters on computing cost, multiscale kernels, UAV targets, and the impact of down-sampling on classification accuracy.…”

Section: B Contributionsmentioning

confidence: 99%

SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network

dil¹,

khan²,

Alam³

et al. 2022

Preprint

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<p>Unmanned air vehicles (UAVs) popularity is on the rise as it enables the services like traffic monitoring, emergency communications, deliveries, and surveillance. However, the unauthorized usage of UAVs (a.k.a drone) may violate security and privacy protocols for security-sensitive national and international institutions. The presented challenges require fast, efficient, and precise detection of UAVs irrespective of harsh weather conditions, the presence of different objects, and their size to enable SafeSpace. Recently, there has been significant progress in using the latest deep learning models, but those models have shortcomings in terms of computational complexity, precision, and non-scalability. To overcome these limitations, we propose a precise and efficient multiscale and multifeature UAV detection network for SafeSpace, i.e., \textit{MultiFeatureNet} (\textit{MFNet}), an improved version of the popular object detection algorithm YOLOv5s. In \textit{MFNet}, we perform multiple changes in the backbone and neck of the YOLOv5s network to focus on the various small and ignored features required for accurate and fast UAV detection. To further improve the accuracy and focus on the specific situation and multiscale UAVs, we classify the \textit{MFNet} into small (S), medium (M), and large (L): these are the combinations of various size filters in the convolution and the bottleneckCSP layers, reside in the backbone and neck of the architecture. This classification helps to overcome the computational cost by training the model on a specific feature map rather than all the features. The results show significant performance gain even for unseen feature maps with minimal loss in accuracy. Results show a significant reduction in training parameters, inference, and increased pattern in FPS and GFLOPs for \textit{MFNet} compared to YOLOv5s. \textit{MFNet-M} performance evaluation in terms of precision, recall, mean average-precision (mAP), and IOU increased around 1.8\%, 2.2\%, 0.9\%, 1.7\% compared to YOLOv5s. Furthermore, \textit{MFNet-M} achieves the best performance with 96.8\% precision, 88.4\% recall, 95.9\% mAP, and 51.1\% IoU for UAV detection. The dataset and code are available as an open source: github.com/ZeeshanKaleem/MultiFeatureNet.</p>

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DIAT-RadSATNet—A Novel Lightweight DCNN Architecture for Micro-Doppler-Based Small Unmanned Aerial Vehicle (SUAV) Targets’ Detection and Classification

Cited by 34 publications

References 36 publications

Indoor Localization based on Short-Range Radar and Rotating Landmarks

Indoor Localization based on Short-Range Radar and Rotating Landmarks

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network

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