Small Radar Cross Section Research Articles

Accurate Detection and Localization of Unmanned Aerial Vehicle Swarms-Enabled Mobile Edge Computing System

Unmanned aerial vehicle (UAV) swarms-enabled mobile edge computing system can be deployed in critical industrial zones for monitoring. Meanwhile, its malicious use may bring great threat to the security, and the accurate detection, and localization are important. UAV swarms show characteristics of the high density, small radar cross section, far range, and time-varying motion, and have posed formidable challenges to the accurate detection and localization. In this article, the accurate detection and localization of UAV swarms are investigated, and an effective method is proposed based on the Dechirp-keystone transform, and frequency-selective reweighted trace minimization. It inherits high robustness of the coherent long-time integration technique and superresolution of the gridless sparse technique. Mathematical analyzes and numerical simulations validate its superiorities in accurate detection and localization of UAV swarms.

Extraction of UAV Sound from a Mixture of Different Sounds

With the rapid advancement of technology of unmanned aerial vehicles (UAVs), security and safety of military and civil infrastructure have been jeopardized. By exploiting the unreliable capabilities of radar to detect low-flying UAVs with small radar cross section, they can be utilized for malicious purposes, e.g., unauthorized surveillance. To detect UAVs, therefore, various other techniques including audio sniffing/analysis of environment have been investigated. It has been shown recently that a UAV can be differentiated from other sound generating objects based on the various features extracted from the sound captured by a single or multiple acoustic sensors. However, features extraction and classification process can only give reliable results if it is fed with a sound generated by a single source. In practice, the captured sound may be a mixture of contribution of two or more different sources. In this paper, we investigate a well-known blind source separation technique, known as projection pursuit, to separate the constituent sounds in a mixture. We have considered a scenario when the different mixed unvoiced sounds are independent and non-Gaussian. The results show that in the given scenario, projection pursuit can be applied successfully to separate UAV sounds from various other unvoiced sounds.

클러터 환경에서 펄스 도플러 레이다 추적 초기화를 위한 신호 강도를 이용하는 RANSAC 처리 기법

We propose a RANdom SAmple Consensus(RANSAC) method using signal intensity for the track initiation of small target detection. The use of a low constant false-alarm rate threshold to detect a small radar cross-section target can cause numerous false alarms. Consequently, the optimal initialization of the target can be difficult. To solve this issue, we propose a sample-splitting RANSAC (S-RANSAC) method, which compensates for the accuracy degradation due to the lack of inlier data in the RANSAC. The key idea of our approach is to split the received plot data according to signal intensity. We compared the performance of the S-RANSAC method with those of a logic-based algorithm, the Hough transform, and the RANSAC method. Consequently, the S-RANSAC method demonstrated a better track initiation performance than the other methods under the given scenario.

함정용 X-밴드 다기능레이다의 잠망경 탐지를 위한 허프 변환(Hough Transform)과 파라미터 선정에 관한 연구

It is difficult to detect a stationary or slow-moving periscope having a small radar cross-section in a maritime environment. This paper presents a periscope detection method using the Hough transform by selecting parameters based on an active electronically scanned array ship-borne X-band multi-function radar. The method was evaluated through modeling and simulation.

A double-threshold target detection method in detecting low slow small target

Abstract With the rapid development of Unmanned Aerial Vehicle (UAV), stealth warship and stealth tank, the mission of radar has become more difficult. The modern radar system must detect and track target through strong clutter from many aspect, such as ground and sea. Radar detection for low slow small （LSS）targets has gradually become a new focus. LSS target has a small Radar Cross Section (RCS) because of its small size, making the SNR of the echo signal very low. At the same time, the Doppler frequency of slow target is very low, making it difficult for conventional signal processing methods to separate the Doppler information from the clutter spectrum. Thus, it does make sense to study how to detect the LSS target in strong clutter efficiently. In this paper, the distribution characteristics of radar clutter and coherent accumulation algorithm for LSS target detection in complex electromagnetic environment are studied. On the base of that, a double-threshold target detection method based on coherent accumulation is proposed. To check out the efficiency of the detection method, an experiment has been conducted, and the data collected was used to test the detection algorithm, and the result has been shown in this paper.

A Complete Automatic Target Recognition System of Low Altitude, Small RCS and Slow Speed (LSS) Targets Based on Multi-Dimensional Feature Fusion.

Low altitude, small radar cross-section (RCS), and slow speed (LSS) targets, for example small unmanned aerial vehicles (UAVs), have become increasingly significant. In this paper, we propose a new automatic target recognition (ATR) system and a complete ATR chain based on multi-dimensional features and multi-layer classifier system using L-band holographic staring radar. We consider all steps of the processing required to make a classification decision out of the raw radar data, mainly including preprocessing for the raw measured Doppler data including regularization and main frequency alignment, selection, and extraction of effective features in three dimensions of RCS, micro-Doppler, and motion, and multi-layer classifier system design. We design creatively a multi-layer classifier system based on directed acyclic graph. Helicopters, small fixed-wing, and rotary-wing UAVs, as well as birds are considered for classification, and the measured data collected by L-band radar demonstrates the effectiveness of the proposed complete ATR classification system. The results show that the ATR classification system based on multi-dimensional features and k-nearest neighbors (KNN) classifier is the best, compared with support vector machine (SVM) and back propagation (BP) neural networks, providing the capability of correct classification with a probability of around 97.62%.

Evaluation of Radar Cross Section in Aerial Operations by using POFACETS for a standard Unmanned Aerial Vehicle (UAV)

In military and common applications, radar assumes an exceptionally noteworthy job in exploring the vehicle, recognizing for flying machines, ships and so on. Radar cross section is a measure of a target's ability to reflect radar signals in the direction of the receiver. It is obvious that RCS depends on the shape of the reflecting objects. In this paper, a brief overview of various radars used in surveillance applications and radar cross section (RCS) measurement techniques are presented. The objective of this paper is to illustrate methods for obtaining the Radar Cross Section (RCS) of a typical Unmanned Aerial Vehicle (UAV) for various frequencies in radar bands. UAV technologies have advanced tremendously and are being developed successfully. These features lead UAV to patrol even in civil airspace for civilian applications. These objects operate with small radar cross-section and/or on low altitude which imposes security threat. Keeping this view the RCS of a UAV which are flying in civilian airspace has to be determined drastically for security purposes. Several shapes are considered for the estimation of RCS. The results on the variations of RCS for the above objects as a function of frequency, aspect angle are presented. The POFACETS plays a crucial role and provides the user an easy–to–use GUI that allows the input of all necessary parameters, while preventing erroneous data input and other user errors. In this paper the RCS of sphere, ellipsoid and UAV are determined.

Radar Measurements of Morphological Parameters and Species Identification Analysis of Migratory Insects

Migratory insect identification has been concerning entomology and pest managers for a long time. Their nocturnal behavior, as well as very small radar cross-section (RCS), makes individual detection challenging for any radar network. Typical entomological radars work at the X-band (9.4 GHz) with a vertical pencil beam. The measured RCS can be used to estimate insect mass and wingbeat frequency, and then migratory insects can be categorized into broad taxon classes using the estimated parameters. However, current entomological radars cannot achieve species identification with any higher precision or confidence. The limited frequency range of current insect radars have precluded the acquisition of more information useful for the identification of individual insects. In this paper, we report an improved measurement method of insect mass and body length using a radar with many more measurement frequencies than current entomological radars. The insect mass and body length can be extracted from the multi-frequency RCSs with uncertainties of 16.31% and 10.74%, respectively. The estimation of the thorax width and aspect ratio can also be achieved with uncertainties of 13.37% and 7.99%, respectively. Furthermore, by analyzing the statistical data of 5532 insects representing 23 species in East China, we found that the correct identification probabilities exceed 0.5 for all of the 23 species and are higher than 0.8 for 15 of the 23 species under the achievable measurement precision of the proposed technique. These findings provide promising improvements of individual parameter measurement for entomological radars and imply a possibility of species identification with higher precision.

Low-Slow-Small (LSS) Target Detection Based on Micro Doppler Analysis in Forward Scattering Radar Geometry.

The increase in drone misuse by civilian apart from military applications is alarming and need to be addressed. This drone is characterized as a low altitude, slow speed, and small radar cross-section (RCS) (LSS) target and is considered difficult to be detected and classified among other biological targets, such as insects and birds existing in the same surveillance volume. Although several attempts reported the successful drone detection on radio frequency-based (RF), thermal, acoustic, video imaging, and other non-technical methods, however, there are also many limitations. Thus, this paper investigated a micro-Doppler analysis from drone rotating blades for detection in a special Forward Scattering Radar (FSR) geometry. The paper leveraged the identified benefits of FSR mode over conventional radars, such as improved radar cross-section (RCS) value irrespective of radar absorbing material (RAM), direct signal perturbation, and high resolutions. To prove the concept, a received signal model for micro-Doppler analysis, a simulation work, and experimental validation are elaborated and explained in the paper. Two rotating blades aspect angle scenarios were considered, which are (i) when drone makes a turn, the blade cross-sectional area faces the receiver and (ii) when drone maneuvers normally, the cross-sectional blade faces up. The FSR system successfully detected a commercial drone and extracted the micro features of a rotating blade. It further verified the feasibility of using a parabolic dish antenna as a receiver in FSR geometry; this marked an appreciable achievement towards the FSR system performance, which in future could be implemented as either active or passive FSR system.

The Extraction of Micro-Doppler Signal With EMD Algorithm for Radar-Based Small UAVs’ Detection

The rotation of rotor blades on small unmanned aerial vehicles (UAVs) introduces micro-Doppler (m-D) signatures to discriminate UAVs from other aircrafts or birds. Compared with the Doppler signal induced by the translation of the platform, the m-D signal, however, is rather weak due to the small radar cross section (RCS) of rotor blades. Moreover, the m-D signal always consists of multiple frequency components, most of which are induced by the vibration of the platform. This paper focuses on the extraction of the rotation signal via the empirical mode decomposition (EMD) algorithm, which would decompose an arbitrary signal into a series of intrinsic mode functions (IMFs). The constitution of IMF is investigated by using the partial differential equations (PDEs) to analyze the distribution of the extrema. For separating the rotation signal, the essence is to unify the frequency of extrema in the corresponding IMF exhibiting the rotation features. First, the fitting Doppler signal is constructed from the data with the Doppler property in the IMF. Second, a criterion is proposed to establish the masking signal, after which the rotation components are extracted from the residual m-D signal via another EMD. Simulations are carried out to demonstrate the effectiveness of the proposed method in solving the mode-mixing problem. The results derived from the measured data validate the feasibility of the proposed approach in the identification of small UAVs.

ALGORITHM OF SIGNAL PROCESSING IN THE RADAR SYSTEM WITH CONTINUOUS FREQUENCY MODULATED RADIATION FOR DETECTION OF SMALL-SIZED AERIAL OBJECTS, ESTIMATION OF THEIR RANGE AND VELOCITY

Nowadays the interest in search of ways of improving the efficiency of small radar cross-section aerial objects detection and localization rises against the background of widespread use of light and unmanned aerial vehi-cles. As a result, researchers pay attention to radar systems (RS) with continuous linear frequency modulation (linear FM) signal. The use of such signals gives the measurable opportunity to reduce radar system peak-speech power and to cut the cost and weightsize parameters of the RS. The paper observes low-power ground based radar implementation prospects for purposes of detection and estimation of motion rates of small-sized aerial objects. The proposed algorithm of radar signals processing enables to simplify the detection of such tar-gets. The paper reveals the structure and defines the steps of the algorithm. The fundamental for the algorithm under consideration is the method of the range-Doppler image composition of the scanned area using digital signal processing. The paper presents the results of the algorithm operation in the low-power RS of C-band radar, obtained by processing of quadrotor echo-signals during the real experiment. The results show successful solvation of the applied problem of detection and tracking on the small-sized aerial object with the radar cross-section equal to less than 0.5 m2 and the spectrum of secondary radiation characterized by the expressed multimodality. The results of the experiment validate the application of the algorithm and demonstrate the possibility of the algorithm implementation in design of portable RS and automated target acquisition centers for detecting and tracking of the small-sized aerial targets (both, single as multi agent) with the information display on operator control panel.

Cyclostationary Phase Analysis on Micro-Doppler Parameters for Radar-Based Small UAVs Detection

Robust radar detection for small unmanned aerial vehicles (UAVs) is a challenging problem, as UAVs tend to fly at slow speed and low altitude, with a small radar cross section. Those properties may lead to difficulties in separating echoes from a significant clutter response. In this case, micro-Doppler (m-D) effect induced by the rotation of the rotor blades would be another preferable signature to enhance the discrimination between ground clutter and UAVs returns, as well as associated correct labels to small UAVs among others (such as birds). However, the detection presents two stubborn problems. First, the m-D signal always consists of multiple components, one of which is induced by the vibration of the platform. It is inevitable that the estimation precision would be affected by the m-D interference. Second, the m-D components would be rather weak. It could not always be observed due to the shelter of Doppler signal induced by the translation of the platform. In this paper, we propose the cyclostationary phase analysis (CPA) to estimate the m-D parameters for radar detection of small UAVs. This method utilizes the phase term of the returned signal as the input of the cyclostationary analysis. It could simplify the procedure of the parameter estimation, as well as reducing the m-D interference. Moreover, since the phase term of the Doppler components is not cyclostationary, the CPA could eliminate the impact of Doppler signal. Simulations and filed experiments are provided to showing an outranking performance than the existing methods in estimation precision.

Magnitude and Spectrum of Electromagnetic Wave Scattered by Small Quadcopter in $X$ -Band

Estimation results of the magnitude and spectrum of electromagnetic wave scattered by small unmanned aerial vehicle with a radar cross section (RCS) of 0.01 m2 obtained by numerical simulation and direct measurements in an anechoic chamber at a frequency of 9 GHz and their comparison are discussed. Effectiveness of the parasitic leakage suppression technique at small RCS measurements has been shown.

W-Band MIMO FMCW Radar System With Simultaneous Transmission of Orthogonal Waveforms for High-Resolution Imaging

This paper presents a multi-input multi-output (MIMO) radar with simultaneously transmitted orthogonal waveforms. The radar operates in W-band, 93.5–94.5 GHz, with frequency-modulated continuous wave (FMCW) signals. The radar is designed to be capable of simultaneous transmission rather than switching transmit channels. A suitable orthogonal waveform transmission method for the FMCW radar is selected and reflected in the transceiver design. The radar consists of a transceiver, antennas, and a signal processor. The design and specifications of each module are described in detail. The transceiver consists of two transmitting channels and two receiving channels. The transmitter is designed to have high transmit power aiming at the detection and imaging of objects with small radar cross section, up to 100–150-m range. A compact horn antenna and two-channel analog-to-digital converter with a high sampling rate are designed and fabricated. The performance of each module is measured and evaluated. The performance of the radar is tested through experiments. The MIMO capability of the radar was verified through the acquisition of the range profiles with respect to four distinct signal paths by distinguishing the simultaneously transmitted orthogonal signals. The experimental results verified that the specification of the radar system is feasible for high-resolution imaging applications.

소형 무인기 탐지를 위한 패시브 레이더망 최적 배치 연구

Current low-altitude radar system often fails to detect small unmanned aerial vehicles (UAV) because of their small radar cross section (RCS) compared with larger targets. As a potential alternative, a passive bistatic radar system has been considered. We study an optimal deployment problem for the passive bistatic radar system. We model this problem as a covering problem, and develop an integer programming model. The objective of the model is to maximize coverage of a passive bistatic radar system. Our model takes into account factors specific to a bistatic radar system, including bistatic RCS and transmitter-receiver pair coverage. Considering bistatic RCS instead of constant RCS is important because the slight difference of RCS value for small UAVs could significantly influence the detection probability. The paired radar coverage is defined by using the concept of gradual coverage and cooperative coverage to represent a realistic environment.

An effective method for incoherent scattering radar's detecting ability evaluation

AbstractIonospheric incoherent scatter radar (ISR), which is used to detect ionospheric electrons and ions, generally, has megawatt class transmission power and hundred meter level antenna aperture. The crucial purpose of this detecting technology is to get ionospheric parameters by acquiring the autocorrelation function and power spectrum of the target ionospheric plasma echoes. Whereas the ISR's echoes are very weak because of the small radar cross section of its target, estimating detecting ability will be significantly instructive and meaningful for ISR system design. In this paper, we evaluate the detecting ability through signal‐to‐noise ratio (SNR). The soft‐target radar equation is deduced to be applicable to ISR, through which we use data from International Reference Ionosphere model to simulate signal‐to‐noise ratio (SNR) of echoes, and then comparing the measured SNR from European Incoherent Scatter Scientific Association and Advanced Modular Incoherent Scatter Radar with the simulation. The simulation results show good consistency with the measured SNR. For ISR, the topic of this paper is the first comparison between the calculated SNR and radar measurements; the detecting ability can be improved through increasing SNR. The effective method for ISR's detecting ability evaluation provides basis for design of radar system.

Atmospheric and Fog Effects on Ultra-Wide Band Radar Operating at Extremely High Frequencies.

The wide band at extremely high frequencies (EHF) above 30 GHz is applicable for high resolution directive radars, resolving the lack of free frequency bands within the lower part of the electromagnetic spectrum. Utilization of ultra-wideband signals in this EHF band is of interest, since it covers a relatively large spectrum, which is free of users, resulting in better resolution in both the longitudinal and transverse dimensions. Noting that frequencies in the millimeter band are subjected to high atmospheric attenuation and dispersion effects, a study of the degradation in the accuracy and resolution is presented. The fact that solid-state millimeter and sub-millimeter radiation sources are producing low power, the method of continuous-wave wideband frequency modulation becomes the natural technique for remote sensing and detection. Millimeter wave radars are used as complementary sensors for the detection of small radar cross-section objects under bad weather conditions, when small objects cannot be seen by optical cameras and infrared detectors. Theoretical analysis for the propagation of a wide “chirped” Frequency-Modulated Continuous-Wave (FMCW) radar signal in a dielectric medium is presented. It is shown that the frequency-dependent (complex) refractivity of the atmospheric medium causes distortions in the phase of the reflected signal, introducing noticeable errors in the longitudinal distance estimations, and at some frequencies may also degrade the resolution.

Small Target Detection Within Sea Clutter Based on Fractal Analysis

Sea clutter refers to the signals returned from the wavy and turbulent rough sea surface. The radar returns from small targets, like submarine periscope or small boats, will be obscured by the sea clutter which makes the straightforward detection of targets, with small radar cross section, a difficult task. The conventional target detection schemes in the presence of sea clutter are mainly based on Hurst exponent, estimated utilizing the fractal characteristics of the received radar data. In the present study, a more robust signature measure is proposed based on fractal analysis of received radar data. The proposed target detection method is applied on actual sea clutter radar data and the performance is compared with the conventional Hurst parameter based method. The simulation results illustrate that the proposed method achieves better detection performance and is robust under varying sea conditions.

Enabling orbit determination of space debris using narrowband radar

A conceptual radar platform design for detecting space debris down to 1 cm in diameter is presented for space situational awareness. Using known imaging algorithms, we show it is possible to overcome the problems associated with the typically small radar cross sections to yield a radar-attributable vector. Given the latter, the problem of matching the observation to a unique piece of debris is tackled independently of the imaging, or the detection, problem.

Tracking unmanned aerial vehicles using a tetrahedral microphone array

Unmanned Aerial Vehicles (UAVs) present a difficult localization problem for traditional radar systems due to their small radar cross section and relatively slow speeds. To help address this problem, the U.S. Army Research Laboratory (ARL) is developing and testing acoustic-based detection and tracking algorithms for UAVs. The focus has been on detection, bearing and elevation angle estimation using either minimum mean square error or adaptive beamforming methods. A model-based method has been implemented which includes multipath returns, and a Kalman filter has been implemented for tracking. The acoustic data were acquired using ARL's tetrahedral microphone array against several UAV's. While the detection and tracking algorithms perform reasonably well, several challenges remain. For example, interference from other sources resulted in lower signal to interference ratio (SIR), which can significantly degrade performance. The presence of multipath nearly always results in greater variance in elevation angle estimates than in bearing angle estimates.