Radar Cross Section Signature Research Articles

Characterization of deorbiting satellites and space debris with radar

During deorbiting, be it natural orbital decay or otherwise (e.g. self-disposal or active debris removal), precise knowledge of the condition of the target is of critical importance. The Tracking and Imaging Radar (TIRA) is an instrument that possesses the ability to accurately determine satellite and space debris orbits while also generating detailed radar images of them.We present recent results from the characterization of deorbiting satellites and space debris employing TIRA. Drawing examples from orbital decay events (Tiangong-1, Soyuz 2.1v R/B), deorbiting (Tiangong-2) and self-disposal operations (Carbonite-1, MicroSCOPE), we demonstrate the potential of TIRA. Furthermore, we focus on the emerging field of active debris removal (ADR), where reliable information of the on-orbit status and attitude motion of potential targets can “make or break” an ADR mission. In this context, the on-orbit status of a JAXA R/B (43682) and a VESPA adapter (39162) are investigated.We employ inverse synthetic aperture radar (ISAR) imaging and 3D modelling of ISAR images, obtained with TIRA, to characterize the status and attitude motion of satellites and space debris. When certain conditions render direct imaging unsuitable, the more powerful L-band tracking radar of TIRA is used to obtain and analyze the temporal evolution of their radar cross-section (RCS). In this work, we employ for the first time, a wavelet time–frequency analysis of such RCS signatures, specifically of NABEO-1 (43692) in LEO and an Ariane 5 rocket body (28904) in geostationary transfer orbit (GTO), to characterize their on-orbit status. The wavelet analysis delivers results in good agreement with more traditional tools, with added information on time localization of periodicities. We are thus able to confirm the tumbling motion of NABEO-1 and constrain the rotational velocity of Ariane 5 upper stage (28904).

Small Fixed-Wing UAV Radar Cross-Section Signature Investigation and Detection and Classification of Distance Estimation Using Realistic Parameters of a Commercial Anti-Drone System

Various types of small drones constitute a modern threat for infrastructure and hardware, as well as for humans; thus, special-purpose radar has been developed in the last years in order to identify such drones. When studying the radar signatures, we observed that the majority of the scientific studies refer to multirotor aerial vehicles; there is a significant gap regarding small, fixed-wing Unmanned Aerial Vehicles (UAVs). Driven by the security principle, we conducted a series of Radar Cross Section (RCS) simulations on the Euclid fixed-wing UAV, which has a wingspan of 2 m and is being developed by our University. The purpose of this study is to partially fill the gap that exists regarding the RCS signatures and identification distances of fixed-wing UAVs of the same wingspan as the Euclid. The software used for the simulations was POFACETS (v.4.1). Two different scenarios were carried out. In scenario A, the RCS of the Euclid fixed-wing UAV, with a 2 m wingspan, was analytically studied. Robin radar systems’ Elvira Anti Drone System is the simulated radar, operating at 8.7 to 9.65 GHz; θ angle is set at 85° for this scenario. Scenario B studies the Euclid RCS within the broader 3 to 16 Ghz spectrum at the same θ = 85° angle. The results indicated that the Euclid UAV presents a mean RCS value (σ ¯) of −17.62 dBsm for scenario A, and a mean RCS value (σ ¯) of −22.77 dBsm for scenario B. These values are much smaller than the values of a typical commercial quadcopter, such as DJI Inspire 1, which presents −9.75 dBsm and −13.92 dBsm for the same exact scenarios, respectively. As calculated in the study, the Euclid UAV can penetrate up to a distance of 1784 m close to the Elvira Anti Drone System, while the DJI Inspire 1 will be detected at 2768 m. This finding is of great importance, as the obviously larger fixed-wing Euclid UAV will be detected about one kilometer closer to the anti-drone system.

Comparative Analysis of Radar-Cross-Section- Based UAV Recognition Techniques

This work investigates the problem of unmanned aerial vehicle (UAV) recognition using their radar cross section (RCS) signature. The RCS of six commercial UAVs is measured at 15 and 25 GHz in an anechoic chamber for both vertical–vertical (VV) polarization and horizontal–horizontal (HH) polarization. The RCS signatures are used to train 15 different recognition algorithms, each belonging to one of three different categories: statistical learning (SL), machine learning (ML), and deep learning (DL). The study shows that, while the average accuracy of all the algorithms increases with the signal-to-noise ratio (SNR), the ML algorithm achieved better accuracy than the SL and DL algorithms. For example, the classification tree ML achieves an accuracy of 98.66% at 3-dB SNR using the 15-GHz VV-polarized RCS test data from the UAVs. We investigate the recognition accuracy using the Monte Carlo analysis with the aid of boxplots, confusion matrices, and classification plots. On average, the accuracy of the classification tree ML model performed better than the other algorithms, followed by Peter Swerling’s statistical models and the discriminant analysis ML model. In general, the accuracy of the ML and SL algorithms outperformed the DL algorithms (Squeezenet, Googlenet, Nasnet, and Resnet 101) considered in the study. Furthermore, the computational time of each algorithm is analyzed. The study concludes that, while the SL algorithms achieved good recognition accuracy, the computational time was relatively long when compared to the ML and DL algorithms. Also, the study shows that the classification tree achieved the fastest average recognition time of about 0.46 ms.

High capacity chipless RFID tags for biomass tracking application

AbstractThe design of a low-cost, flexible, miniaturized, and a high code density chipless radio-frequency identification (RFID) tag is presented as a solution for tracking the transportation of biomass fuel pellets. The performance of the tag is presented and demonstrates the applicability of the design for different material systems, while maintaining a compact size of 5.06 cm2. The tag consists of nested concentric hexagonal elements and a central spiral resonator suitable for ID encoding. The tag presented demonstrates code density of 3.6-bits/cm2, possesses angular stability up to 60°, and high radar cross section (RCS). The tag performance was also observed for tracking 5 kg of fly-ash biomass. Additionally, as the tag mass mostly consists of FR4, PET, or Taconic TLX-0 with a minute mass of either copper, gold, or silver, the tag can be easily combusted and disposed of during biomass combustion. The novel features of this tag are the combination of hexagonal and spiral shape slots for maximum space utilization thereby achieving high RCS signatures along with high code density. All these properties of the proposed chipless RFID tag provide a pioneering pathway for a real-time biomass tracking application.

Multistate active control RCS signature for the continuous adjustment absorber/reflector transformation applications.

Smart structures with tunable electromagnetic characteristics are required for camouflaging high-value targets, such as land warfare moving equipment, in continuously changing complex electromagnetic environments. The targets must control their radar cross section (RCS) to avoid detection and tracking. Frequency selective surfaces (FSSs) are the next-generation smart structures in which active RCS control is realized via impedance loading. In this paper, a multistate transformable FSS absorber/reflector that operates in the 3.9-11.0 GHz band is introduced and analyzed. The reflectivity amplitude of this absorber/reflector could be smoothly changed from 0 to -10 dB in 6.0-8.0 GHz. Each unit cell of the FSS structure consisted of four symmetrical diamond-shaped patterns, and the adjacent units were connected by PIN diodes. The absorption intensity of the FSS could be changed from 0 to -10 dB by adjusting the PIN bias voltage, which was applied via a simple bias network. The multistate switching characteristic of the FSS was verified by simulations and measurements. The results showed that adjustable absorbing intensity and switchable working states are the desirable characteristics that allow high-value targets to adapt to changing electromagnetic environments. Our work will bridge the gap between the available stealth strategies and practical applications, such as moving stealth vehicles.

Practical Investigation of a MIMO radar system capabilities for small drones detection

The latest progress of the multiple-input multiple-output (MIMO) radar system developed for small drones detection at Beijing Institute of Technology is presented herein. A low-cost S-band MIMO scanning radar system is designed for the detection of small drones. A practical design optimisation and implementation of a sparse array covering the whole airspace, and a target-based MIMO radar array calibration method to improve the signal-to-noise ratio of the target are proposed. An experiment in the microwave chamber was conducted to verify the designed sparse-array beam-pattern, and measurement campaigns for small drones were performed to test the system performance where the radar achieved detection and tracking of a small DJI-Phantom 4 drone at a range of up to 5 km. Target features, including micro-Doppler and radar cross-section signatures were extracted to assist target classification. The experimental results indicate that the system has promising capability in small drones detection and tracking, despite the challenges of low dwell time on targets due to the scanning nature of the system.

Deep Learning-Based Drone Classification Using Radar Cross Section Signatures at mmWave Frequencies

This paper presents drone classification at millimeter-wave (mmWave) radars using the deep learning (DL) technique. The adoption mmWave technology in radar systems enables better resolution and aid in detecting smaller drones. Using radar cross-section (RCS) signature enables us to detect malicious drones and suitable action can be taken by respective authorities. Existing drone classification converts the RCS signature into images and then performs drone classification using a convolution neural network (CNN). Converting every signature into an image induces additional computation overhead; further CNN model is trained considering fixed learning rate. Thus, when using CNN-based drone classification under a highly dynamic environment exhibit poor classification accuracy. This paper present an im- proved long short-term memory (LSTM) by introducing a weight optimization model that can reduce computation overhead by not allowing the gradient to not flow through hidden states of the LSTM model. Further, present adaptive learning rate optimizing (ALRO) model for training the LSTM model. Experiment outcome shows LSTM-ALRO achieves much better drone detection accuracies when compared with the existing CNN-based drone classification model.

Analyzing Radar Cross Section Signatures of Diverse Drone Models at mmWave Frequencies

In this work, we present quasi-monostatic Radar Cross Section measurements of different Unmanned Aerial Vehicles at 26-40 GHz. We study the Radar Cross Section signatures of nine different multi-rotor platforms as well as a single Lithium-ion Polymer battery. These results are useful in the design and testing of radar systems which employ millimeter-wave frequencies for superior drone detection. The data shows how radio waves are scattered by drones of various sizes and what impact the primary construction material has on the received Radar Cross Section signatures. Matching our intuition, the measurements confirm that larger drones made of carbon fiber are easier to detect, whereas drones made from plastic and styrofoam materials are less visible to the radar systems. The measurement results are published as an open database, creating an invaluable reference for engineers working on drone detection.

Ultrabroadband Monostatic/Bistatic RCS Reduction via High-Entropy Phase-Encoded Polarization Conversion Metasurfaces

A randomized phase-encoded polarization conversion metasurface (RPPCM) is designed in this letter to reduce both monostatic and bistatic radar cross section (RCS) levels of a microstrip antenna working at 2.45 GHz, simultaneously. A fast entropy-based method is elaborately utilized to extract the optimum diffusion layout with low time consumption. The designed RPPCM exposes low monostatic and bistatic RCS signatures (RCSR $>$ 10 dB) in a wide frequency range from 8.2 to 25.6 GHz (BW = 102%). The performance of the proposed RPPCM has been significantly improved with respect to the similar designs, as compared through this letter. To validate the performance of our design, a low-scattering microstrip antenna is also designed, fabricated, and measured, in which by loading the RPPCM around the patch of antenna, in addition to rotating the incident wave polarization, it is capable of distributing the incident power into many undetectable scattered beams with minor influences on radiation specifications. The experimental results of the fabricated prototype corroborate well the corresponding numerical simulations, where RCSR $>$ 5 dB has been obtained in a broadband region from 6.7 to 24 GHz (BW = 113%).

Numerical Simulation and Experimental Analysis of Small Drone Rotor Blade Polarimetry Based on RCS and Micro-Doppler Signature

Detecting and tracking small drones is significant for security requirements, but is challenging since small drones typically have a low radar cross section (RCS), small size, and hovering ability. Polarimetric analysis of returned radio wave is of great significance in detection and track of small drones. In this letter, the polarimetric analysis based on RCS and micro-Doppler signature of small drone rotor blade is first studied in VHF/UHF band. Both the simulated and measured RCS results confirm that the horizontal–horizontal polarization is preferred for drone detection in low-altitude angles at this frequency band. Moreover, the method of moments, a rigorous numerical method based on integral equation, is implemented to simulate the micro-Doppler signature generated by the rotating blade in VHF/UHF band. The results show that it is feasible to obtain the rotating speed from the micro-Doppler measurement, which is also verified by the experiment.

Scattering characteristics of finite arrays of cylindrical cavities in an infinite ground plane

In this paper, we investigate the scattering characteristics of finite one-dimensional (1D) and two-dimensional arrays of cylindrical cavities embedded within an infinite perfect electric conducting ground plane. Fundamental radar cross-section (RCS) features of said arrays are illustrated, with the RCS computed with respect to the scattered (diffracted) field component, obtained directly from the aperture currents. The aperture currents on the finite structure are computed using a hybrid space/spectral-domain MoM formulation. The complete set of (TE,TM) cylindrical eigenmodes are used to represent the unknowns. The dependence of the current distribution on the source polarization is shown for the canonical cavity. Three-dimensional plots of the RCS signature of a 1D finite array illustrate the unique radiation characteristics of such structures, e.g., the constant-directivity surfaces form cones about the array axis in 3D space. The effects of a superstrate layer on the radiation behavior of a 2D finite array are also shown. We find that the layer can in fact either enhance or diminish the radiation, depending on the source polarization. It also tends to reduce endfire radiation in all cases. The scattering signature of large finite arrays is computed approximately by the introduction of an active element factor defined specifically for scattering problems. Using the MoM solution of a reduced window array, we obtain the far-field patterns of larger structures by a simple computation of the array factor. An improvement to this approximation makes use of an exterior equivalent source, which models the localized edge effects. Finally, the use of nonuniform arrays for sidelobe suppression and 2D "masked" structures is illustrated.

Aircraft identification from RCS measurement using an orthogonal transform

A comparative study on target identification using the radar cross section (RCS) signature of an aircraft in both the frequency domain and the range domain is conducted. A maximum likelihood method is employed to perform the identification process. Generalised likelihood identification when the received RCS signal is attenuated by an unknown amount is also examined. Target identification could be quite computationally intensive since a large number of library reference signatures may have to be searched to declare an identification. The use of an orthogonal transform is proposed to reduce the computational requirement. It is found that the discrete cosine transform is very effective in compacting the RCS signature in the frequency domain, and the Haar transform is more efficient in the range domain. The application of orthogonal transforms can reduce the computational complexity by at least 50% while maintaining the same identification accuracy.

The Benefits and Costs of Stealth From The Aircraft Designer’s Viewpoint

The reduction or other control of an aircraft’s radar, infrared, visual and acoustic signatures can greatly improve its survival, resulting in improved weapons` effectiveness. Although radar and infrared stealth are important, other aspects of low observability (i.e. acouctic and visual) cannot be ignored. However, this paper, for reasons of brevity, focuses on control of radar and infrared signatures. The topics of: benefits of signature control; contributions of an aircraft to its radar cross section (RCS) and infrared (IR) signature; methods to reduce RCS and IR signature; penalties/costs of signature reduction, in terms of performance, volume, weight and maintenance; and use of radar absorbing materials are addressed. The conclusion is that while signature control is important, there are penalties to be paid. Signature reduction has become yet another factor to be concidered in the series of compromises made during aircraft design trade-offs.

Application of the nonparametric c1statistical test to radar cross section signature comparisons

The nonparametric rank order c <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> test is proposed and evaluated as a method for obtaining a quantitative, numerical measure of the degree of similarity existing between two radar cross section signatures as a function of aspect angle.