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

Measurements of the Multistatic X&L Band Radar Signatures of UAVS

This paper illustrates the results of a series of measurements of multistatic radar signatures of small UAVs at L and X band. The system employed was the multistatic multiband radar system, NeXtRAD, consisting of one monostatic transmitter-receiver and two bistatic receivers. Results demonstrate the capability of the system of recording bistatic data with baselines and two-way bistatic range of the order of few kilometres

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 Birds and UAVs Based on Radar Polarimetry

This letter aims to show the potential of using polarimetric parameters to distinguish between large birds and unmanned aerial vehicles (UAVs) of comparable size in the context of a modern long-range air defense radar. Time is a critical resource in such systems, and techniques for robust noncooperative target recognition not relying on spatial resolution or long dwell times are highly desired. Furthermore, methods less dependent on target micromotion are, in many cases, required. Methods exploiting polarimetric features are shown to have potential in both cases. An experiment in S-band shows that a simple nearest-neighbor classifier can achieve good separation between UAVs and birds both with and without detectable micromotion based on a set of polarimetric parameters alone

Measurements and discrimination of drones and birds with a multi‐frequency multistatic radar system

This article presents the results of a series of measurements of multistatic radar signatures of small UAVs at L‐ and X‐bands. The system employed was the multistatic multiband radar system, NeXtRAD, consisting of one monostatic transmitter‐receiver and two bistatic receivers. NeXtRAD is capable of recording simultaneous bistatic and monostatic data with baselines and two‐way bistatic range of the order of a few kilometres. The paper presents an empirical analysis with range‐time plots and micro‐Doppler signatures of UAVs and birds of opportunity recorded at several hundred metres of distance. A quantitative analysis of the overall signal‐to‐noise ratio is presented along with a comparison between the power of the signal scattered from the drone body and blades. A simple study with empirically obtained features and four supervised‐learning classifiers for binary drone versus non‐drone separation is also presented. The results are encouraging with classification accuracy consistently above 90% using very simple features and classification algorithms

Measurements of multistatic X&L band radar signatures of UAVs

This paper illustrates the results of a series of measurements of multistatic radar signatures of small UAVs at L and X band. The system employed was the multistatic multiband radar system, NeXtRAD, consisting of one monostatic transmitter-receiver and two bistatic receivers. Results demonstrate the capability of the system of recording bistatic data with baselines and two-way bistatic range of the order of few kilometres

Try Living in the Real World: the importance of experimental radar systems and data collection trials

While simulations of increasingly high fidelity are an important tool in radar science, experimentation is still needed as a source of validation for simulation, to explore complex phenomena which cannot be accurately simulated and ultimately in turning theory and simulation into a real world system with real world applications. Experimental systems can range from laboratory based, installations on the ground with limited fields of view all the way up to flying demonstrators which may be prototypes for radar products. In this paper we will discuss the importance of experimentation in the development of radar science and radar products with examples of systems used by a sub-set of the members of the UK EMSIG

Terahertz Micro-Doppler Radar for Detection and Characterization of Multicopters

abstract: The micromotions (e.g. vibration, rotation, etc.,) of a target induce time-varying frequency modulations on the reflected signal, called the micro-Doppler modulations. Micro-Doppler modulations are target specific and may contain information needed to detect and characterize the target. Thus, unlike conventional Doppler radars, Fourier transform cannot be used for the analysis of these time dependent frequency modulations. While Doppler radars can detect the presence of a target and deduce if it is approaching or receding from the radar location, they cannot identify the target. Meaning, for a Doppler radar, a small commercial aircraft and a fighter plane when gliding at the same velocity exhibit similar radar signature. However, using a micro-Doppler radar, the time dependent frequency variations caused by the vibrational and rotational micromotions of the two aircrafts can be captured and analyzed to discern between them. Similarly, micro-Doppler signature can be used to distinguish a multicopter from a bird, a quadcopter from a hexacopter or a octacopter, a bus from a car or a truck and even one person from another. In all these scenarios, joint time-frequency transforms must be employed for the analysis of micro-Doppler variations, in order to extract the targets’ features. Due to ample bandwidth, THz radiation provides richer radar signals than the microwave systems. Thus, a Terahertz (THz) micro-Doppler radar is developed in this work for the detection and characterization of the micro-Doppler signatures of quadcopters. The radar is implemented as a continuous-wave (CW) radar in monostatic configuration and operates at a low-THz frequency of 270 GHz. A linear time-frequency transform, the short-time Fourier transform (STFT) is used for the analysis the micro-Doppler signature. The designed radar has been built and measurements are carried out using a quadcopter to detect the micro-Doppler modulations caused by the rotation of its propellers. The spectrograms are obtained for a quadcopter hovering in front of the radar and analysis methods are developed for characterizing the frequency variations caused by the rotational and vibrational micromotions of the quadcopter. The proposed method can be effective for distinguishing the quadcopters from other flying targets like birds which lack the rotational micromotions.Dissertation/ThesisMasters Thesis Electrical Engineering 201