1,738 research outputs found
Introduction to Drone Detection Radar with Emphasis on Automatic Target Recognition (ATR) technology
This paper discusses the challenges of detecting and categorizing small
drones with radar automatic target recognition (ATR) technology. The authors
suggest integrating ATR capabilities into drone detection radar systems to
improve performance and manage emerging threats. The study focuses primarily on
drones in Group 1 and 2. The paper highlights the need to consider kinetic
features and signal signatures, such as micro-Doppler, in ATR techniques to
efficiently recognize small drones. The authors also present a comprehensive
drone detection radar system design that balances detection and tracking
requirements, incorporating parameter adjustment based on scattering region
theory. They offer an example of a performance improvement achieved using
feedback and situational awareness mechanisms with the integrated ATR
capabilities. Furthermore, the paper examines challenges related to one-way
attack drones and explores the potential of cognitive radar as a solution. The
integration of ATR capabilities transforms a 3D radar system into a 4D radar
system, resulting in improved drone detection performance. These advancements
are useful in military, civilian, and commercial applications, and ongoing
research and development efforts are essential to keep radar systems effective
and ready to detect, track, and respond to emerging threats.Comment: 17 pages, 14 figures, submitted to a journal and being under revie
An introduction to radar Automatic Target Recognition (ATR) technology in ground-based radar systems
This paper presents a brief examination of Automatic Target Recognition (ATR)
technology within ground-based radar systems. It offers a lucid comprehension
of the ATR concept, delves into its historical milestones, and categorizes ATR
methods according to different scattering regions. By incorporating ATR
solutions into radar systems, this study demonstrates the expansion of radar
detection ranges and the enhancement of tracking capabilities, leading to
superior situational awareness. Drawing insights from the Russo-Ukrainian War,
the paper highlights three pressing radar applications that urgently
necessitate ATR technology: detecting stealth aircraft, countering small
drones, and implementing anti-jamming measures. Anticipating the next wave of
radar ATR research, the study predicts a surge in cognitive radar and machine
learning (ML)-driven algorithms. These emerging methodologies aspire to
confront challenges associated with system adaptation, real-time recognition,
and environmental adaptability. Ultimately, ATR stands poised to revolutionize
conventional radar systems, ushering in an era of 4D sensing capabilities
Modulation recognition of low-SNR UAV radar signals based on bispectral slices and GA-BP neural network
In this paper, we address the challenge of low recognition rates in existing methods for radar signals from unmanned aerial vehicles (UAV) with low signal-to-noise ratios (SNRs). To overcome this challenge, we propose the utilization of the bispectral slice approach for accurate recognition of complex UAV radar signals. Our approach involves extracting the bispectral diagonal slice and the maximum bispectral amplitude horizontal slice from the bispectrum amplitude spectrum of the received UAV radar signal. These slices serve as the basis for subsequent identification by calculating characteristic parameters such as convexity, box dimension, and sparseness. To accomplish the recognition task, we employ a GA-BP neural network. The significant variations observed in the bispectral slices of different signals, along with their robustness against Gaussian noise, contribute to the high separability and stability of the extracted bispectral convexity, bispectral box dimension, and bispectral sparseness. Through simulations involving five radar signals, our proposed method demonstrates superior performance. Remarkably, even under challenging conditions with an SNR as low as −3 dB, the recognition accuracy for the five different radar signals exceeds 90%. Our research aims to enhance the understanding and application of modulation recognition techniques for UAV radar signals, particularly in scenarios with low SNRs
ROADS—Rover for Bituminous Pavement Distress Survey: An Unmanned Ground Vehicle (UGV) Prototype for Pavement Distress Evaluation
Maintenance has a major impact on the financial plan of road managers. To ameliorate road conditions and reduce safety constraints, distress evaluation methods should be efficient and should avoid being time consuming. That is why road cadastral catalogs should be updated periodically, and interventions should be provided for specific management plans. This paper focuses on the setting of an Unmanned Ground Vehicle (UGV) for road pavement distress monitoring, and the Rover for bituminOus pAvement Distress Survey (ROADS) prototype is presented in this paper. ROADS has a multisensory platform fixed on it that is able to collect different parameters. Navigation and environment sensors support a two-image acquisition system which is composed of a high-resolution digital camera and a multispectral imaging sensor. The Pavement Condition Index (PCI) and the Image Distress Quantity (IDQ) are, respectively, calculated by field activities and image computation. The model used to calculate the I-ROADS index from PCI had an accuracy of 74.2%. Such results show that the retrieval of PCI from image-based approach is achievable and values can be categorized as "Good"/"Preventive Maintenance", "Fair"/"Rehabilitation", "Poor"/"Reconstruction", which are ranges of the custom PCI ranting scale and represents a typical repair strategy
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