This paper presents a comprehensive review of current literature on drone detection and classification using machine learning with different modalities. This research area has emerged in the last few years due to the rapid development of commercial and recreational drones and the associated risk to airspace safety. Addressed technologies encompass radar, visual, acoustic, and radio-frequency sensing systems. The general finding of this study demonstrates that machine learning-based classification of drones seems to be promising with many successful individual contributions. However, most of the performed research is experimental and the outcomes from different papers can hardly be compared. A general requirement-driven specification for the problem of drone detection and classification is still missing as well as reference datasets which would help in evaluating different solutions.

/pdf/machine-learning-based-drone-detection-and-classification-21txfq2ocl.pdf

Machine Learning-Based Drone Detection and Classification: State-of-the-Art in Research

Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.

https://www.mdpi.com/1424-8220/19/23/5170/pdf

A Survey on Machine-Learning Techniques for UAV-Based Communications

Thank you for downloading two dimensional signal and image processing. As you may know, people have look hundreds times for their chosen novels like this two dimensional signal and image processing, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some infectious virus inside their computer. two dimensional signal and image processing is available in our book collection an online access to it is set as public so you can download it instantly. Our digital library spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the two dimensional signal and image processing is universally compatible with any devices to read.

Two Dimensional Signal And Image Processing

With the increasing number of drones, the danger of their illegal use has become relevant. This has necessitated the creation of automatic drone protection systems. One of the important tasks solved by these systems is the reliable detection of drones near guarded objects. This problem can be solved using various methods. From the point of view of the price-quality ratio, the use of video cameras for a drone detection is of great interest. However, drone detection using visual information is hampered by the large similarity of drones to other objects, such as birds or airplanes. In addition, drones can reach very high speeds, so detection should be done in real time. This paper addresses the problem of real-time drone detection with high accuracy. We divided the drone detection task into two separate tasks: the detection of moving objects and the classification of the detected object into drone, bird, and background. The moving object detection is based on background subtraction, while classification is performed using a convolutional neural network (CNN). The experimental results showed that the proposed approach can achieve an accuracy comparable to existing approaches at high processing speed. We also concluded that the main limitation of our detector is the dependence of its performance on the presence of a moving background.

https://www.mdpi.com/1424-8220/20/14/3856/pdf

Real-Time and Accurate Drone Detection in a Video with a Static Background.

Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.

Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

Deployment of internet of things (IoT) devices, wireless sensors, and sensor networks, in agriculture can be a great help in monitoring environment and growing crops; and having a network to support those devices is necessary to successfully utilize those resources. Recently, low-power wide area network (LPWAN) have been recognized as an appropriate technology for agriculture use. LoRa is a representative network of low-power wide area network (LPWAN). It can be applied to IoT for agriculture due to its long range and low power capabilities. LoRa network performance is highly affected by physical layer parameters and the environment. While LPWANs have been around for a number of years, they are still considered an emerging technology and have had few scientific studies performed. Currently, most studies have shown LoRa communication capabilities in urban, mountainous, and maritime areas, with little focus on agriculture use cases. Tree farming is a long-term investment, requiring careful monitoring to mitigate loss; therefore, this paper provides an analysis about the impact of variant physical layer parameters on performance of LoRa networks in a tree farm. Overall, the LoRa communication range was smaller than the theoretically expected range. Some PHY factors, spreading factor and coding rate, showed a clear impact on LoRa performance. Actual data reliability was inconsistent at varying distances and PHY configurations, unlike the consistency found in the RSSI reported by the radios. Additional experiments show the Fresnel Zone still affects LoRa networks. All experiments were performed in an Indiana rural tree plantation in the United States. Each node was set differently by modulating spreading factor, bandwidth and coding rate using 915MHz and 13 dBm transmit power at variant distance.

An experimental LoRa performance evaluation in tree farm

Class 1 unmanned aerial vehicles (UAVs), known as drones, have become popular and accessible, which makes them tools for malicious purposes. As a result, there is an increasing demand for an effective defense system that can detect UAVs. In this paper, a UAV detection system with multiple acoustic nodes using machine learning models is proposed along with an empirically optimized configuration of the nodes for deployment. Features including Mel-frequency cepstral coefficients (MFCC) and short-time Fourier transform (STFT) were used for training. Support vector machines (SVM) and convolutional neural networks (CNN) were trained with the data collected in person. Experiments were done to evaluate models’ ability to find the path of the UAV that was flying. Sensing nodes were placed in four different configurations and the best of test set was chosen which maximizes the detection range without blind spots. STFT-SVM model showed the best performance and a semi-circle formation with 75 meters distance between a node and the protected area is found to be the optimized configuration.

UAV Detection System with Multiple Acoustic Nodes Using Machine Learning Models

A system and method are provided for automatically aiming an antenna to communicate with a remote broadband wireless communication device.

Antenna aiming system and method for broadband wireless access

We discuss the proof of concept that gives mobile robotic units the ability to provide a mobile wireless mesh network providing wireless service to end-clients and also demonstrate the ability to increase the throughput of this mobile wireless mesh system by autonomously reducing the hop count required for network traffic to transit through. In doing so, this proof-of-concept contributes to future development of a robust system which can be deployed and utilized in different situations and industry.

Using Mobile Robots to Establish Mobile Wireless Mesh Networks and Increase Network Throughput

In our previous work, we presented autonomous, self-organizing wireless networks using multiple mobile robots, which enabled the desired wireless coverage in the form of a mesh network and a point-to-point network. Capacities to cover the desired area using multiple robots, all carrying omnidirectional antennas, were demonstrated. However, the use of omnidirectional antennas did not prove effective in covering a sufficiently large distance. Hence, in this paper, we introduce the use of directional antennas to increase the range of the wireless network, and later apply this concept to extending a wireless network over complex terrain. As the first step to using directional antennas, we will present a pattern-based search algorithm to address the problem of self-orientation of a Directional Wireless Network (DWN). The algorithm will find the orientation of antennas that enables them to receive the best possible Received Signal Strength Indication (RSSI). Preliminary experimental results will be shown that demonstrate the ability of the algorithm, assisted by mobile robots with pan-tilt devices for the antennas, to automatically find the maximum RSSI available. Finally, we will conclude with a discussion of findings from this paper and applications of the use of directional antennas.

Self-orientation of directional antennas, assisted by mobile robots, for receiving the best wireless signal strength

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Anthony Smith

Author Tools

Chat about Author