Jet Engine Modulation Research Articles

Detection of Micro-Doppler Signals of Drones Using Radar Systems with Different Radar Dwell Times

Not any radar dwell time of a drone radar is suitable for detecting micro-Doppler (or jet engine modulation, JEM) produced by the rotating blades in radar signals of drones. Theoretically, any X-band drone radar system should detect micro-Doppler of blades because of the micro-Doppler effect and partial resonance effect. Yet, we analyzed radar data detected by three radar systems with different radar dwell times but similar frequency and velocity resolution, including Radar−α, Radar−β, and Radar−γ with radar dwell times of 2.7 ms, 20 ms, and 89 ms, respectively. The results indicate that Radar−β is the best radar for detecting micro-Doppler (i.e., JEM signals) produced by the rotating blades of a quadrotor drone, DJI Phantom 4, because the detection probability of JEM signals is almost 100%, with approximately 2 peaks, whose magnitudes are similar to that of the body Doppler. In contrast, Radar−α can barely detect any micro-Doppler, and Radar−γ detects weak micro-Doppler signals, whose magnitude is only 10% of the body Doppler’s. Proper radar dwell time is the key to micro-Doppler detection. This research provides an idea for designing a cognitive micro-Doppler radar by changing radar dwell time for detecting and tracking micro-Doppler signals of drones.

Comparison of Radar Signatures from a Hybrid VTOL Fixed-Wing Drone and Quad-Rotor Drone

Current studies rarely mention radar detection of hybrid vertical take-off and landing (VTOL) fixed-wing drones. We investigated radar signals of an industry-tier VTOL fixed-wing drone, TX25A, compared with the radar detection results of a quad-rotor drone, DJI Phantom 4. We used an X-band pulse-Doppler phased array radar to collect tracking radar data of the two drones in a coastal area near the Yellow Sea in China. The measurements indicate that TX25A had double the values of radar cross-section (RCS) and flying speed and a 2 dB larger signal-to-clutter ratio (SCR) than DJI Phantom 4. The radar signals of both drones had micro-Doppler signals or jet engine modulation (JEM) produced by the lifting rotor blades, but the Doppler modulated by the puller rotor blades of TX25A was undetectable. JEM provides radar signatures such as the rotating rate, modulated by the JEM frequency spacing interval and the number of blades for radar automatic target recognition (ATR), but also interferes with the radar tracking algorithm by suppressing the body Doppler. This work provides an a priori investigation of new VTOL fixed-wing drones and may inspire future research.

A Novel Automatic Algorithm for Estimating the Jet Engine Blade Number of Insufficient JEM Signals

One of the major issues in multifunction radars is time resource allocation to maximize the radar’s ability. If jet engine modulation (JEM) is more efficiently performed in an insufficient dwell-time environment, the remaining time can be allocated for other tasks. This study presents a novel automatic algorithm for estimating the jet engine blade number of insufficient JEM signals. We employed a harmonic selection rule and a modified empirical mode decomposition (EMD) with an adaptive low-pass filtering. For a refined autocorrelation waveform, the analysis focuses on a desirable combination of intrinsic mode functions derived from the modified EMD. The approach is significant because it enables reliable estimation despite the insufficient JEM signal. Also, the proposed algorithm is innovative because it uses only the time-domain method, not the frequency-domain method. The application is expected to enhance the efficiency of radar resource management.

A Fast Sequential MLE Algorithm for Jet Engine Classification With Radar

Radar signals scattered by an operating jet engine contain a unique signature that can be used to classify the type of engine, and therefore the aircraft. We present a new algorithm based on the maximum likelihood estimation (MLE) principle that can identify the number of blades of the rotors in the jet engine from the returned radar signal. Unlike the conventional algorithms, the proposed algorithm avoids the estimation/inspection of the autocorrelation of radar signal and utilizes the property of jet engine modulation (JEM) harmonics together with the successive interference cancellation (SIC) technique in the multi-dimensional search, which saves about 66.7% computing time compared to the autocorrelation based algorithm (specifically, the empirical mode decomposition (EMD) based algorithm). The proposed algorithm is assessed with synthetic data as well as real radar data using a mock-up jet engine. Specifically, by evaluating the mean-squared-errors and Cramer-Rao lower bound, we verify that the proposed SIC algorithm performs better than previously known jet engine-classification algorithms.

Comparison of micro‐Doppler signatures registered using RBM of helicopters and WSM of vehicles

Jet engine modulation (JEM) are micro-Doppler features obtained from the radar data of aircraft, including jet aircraft and helicopters, and generally viewed as unique phenomenon suitable for identifying aerial targets. The authors demonstrate however, that JEM are not unique, and that the rotating wheel spokes on the wheels of ground vehicles produce a modulation similar to the rotating rotor blades of jet engines and helicopter rotor blades. They also develop a mathematical model to analyse the radar signal scattering mechanism of wheel spokes, and also employ a Ku-band Doppler radar system to collect radar echoes from a wheeled vehicle and a helicopter. Both the theoretical and experimental results verify the existence of rotor blade modulation (RBM) phenomena from helicopters and wheel spoke modulation (WSM) phenomena from vehicles. Both RBM and WSM are similar to JEM features. This fact indicates that JEM is a necessary but insufficient condition for the identification of aerial targets, and additional recognition features inherent in surface surveillance radar are needed to distinguish the JEM spectra arising from aerial targets and ground vehicles.

Robust and fast algorithm for extracting the periodic feature from jet engine modulation signals

Jet engine modulation (JEM), a modulation phenomenon induced by the rotating structure of jet engines, is a representative feature extracted from the radar returns for aircraft target recognition. As one of the most significant periodic characteristics of JEM, the chopping frequency plays an important role in aircraft target recognition. However, existing methods either need complicated logical algorithms to get an accurate estimation of the chopping frequency or exploit the chopping frequency as priori information for further feature extraction, and neither of these two scenarios is practical. Here, the authors propose a robust and fast algorithm for extracting the chopping frequency, which uses wavelet decomposition combined with autocorrelation to process the analytic form of the JEM signal. Application results of measured JEM signals demonstrate that the proposed algorithm is effective and practical for stable and accurate extraction of the periodic feature from JEM signals.

약한 제트 엔진 변조 신호의 Spool Rate 추출을 위한 High-Pass Filtering 기반의 빠른 전처리 기법

Jet engine modulation(JEM) signals are widely used for target recognition. These signals coming from a potentially hostile aircraft provide specific information about the jet engine. In order to obtain the number of blades, which is uniquely provided by the JEM signal, one must extract the spool rate, which is the rotation speed of the blades. In this paper, we propose an algorithm to extract the spool rate from a weak JEM signal. A criterion is developed to extract the spool rate from the JEM signal by analyzing the intensity of the JEM signal component. The weak signal is first subjected to a high-pass filtering-based process, which modifies it to facilitate spool rate extraction. We then apply a peak detection process and extract the spool rate. The technique is simpler than the existing CEMD or WD method, is accurate, and greatly reduces the time required.

Experimental Observations of Micro-Doppler Signatures With Passive Radar

Passive radar's ability to observe micro-Doppler phenomena from helicopters, fixed-wing propeller aircraft, and jet engine modulation is examined. Experimental results from a digital television based passive radar demonstrate its potential to observe characteristic signatures of multiple types of air targets and estimate helicopters’ primary rotor parameters.

MUSIC 알고리즘을 이용한 JEM 신호의 Chopping 주파수 추출

Jet engine modulation(JEM) signals are widely used in the field of target recognition along with high-range resolution profile and inverse synthetic aperture radar because they provide specific information of the jet engine. To obtain the number of blades of the jet engine, the chopping frequency proportional to the number of blades must be extracted. In the conventional chopping frequency extraction method, an initial threshold value is defined and a method of detecting the chopping peak is used. However, this detection method takes time depending on the signal due to repetitive detection. Thus, in this study, we proposed to extract the chopping frequency using MUltiple SIgnal Classification(MUSIC) algorithm. We applied the MUSIC algorithm to a given JEM signal to find the chopping frequency and determine the blade number candidates. We also applied the MUSIC algorithm to other chopping frequency extractions to determine the score of the candidate groups. Unlike the conventional detection algorithm, which requires repetitive frequency detection, MUSIC algorithm quickly detects the accurate chopping frequency and reduces the calculation time.

Robust and fast algorithm for estimating fundamental periodicity of jet engine modulation signals

Jet engine modulation (JEM), a modulation phenomenon induced by a rotating jet engine compressor, is a representative feature extracted from the radar for aircraft target recognition. In this study, the authors present a robust and fast algorithm for extracting the spool rate, which imparts fundamental periodicity to JEM signals. First, a wavelet decomposition with Meyer wavelet was applied to the analytic form of the JEM signal. Then, the decomposition components were combined to achieve a sufficient energy and minimise the mean of the JEM auto-correlation. Finally, the peak detection algorithm was employed for automatic estimation of the spool rate. Application results of measured JEM signals demonstrated that the proposed algorithm is effective for stable and accurate extraction of the jet engine information.

Automatic feature extraction from jet engine modulation signals based on an image processing method

This study presents an automatic method for extracting the jet engine features from the joint time-frequency (JTF) representation of jet engine modulation (JEM) signals. First, empirical mode decomposition with adaptive low-pass filtering was employed to extract the first harmonic component of the JEM signal. Then, a smoothed pseudo Wigner–Ville distribution (SPWVD) technique was used for acquiring the refined JTF representation. After converting the SPWVD result into an image with RGB colours, the green component was extracted as a representative of the JEM component. Finally, the peaks detected from the extracted green component can represent the jet engine features. The approach proposed in this study is significant because the overall procedures for extracting the jet engine features are not manual but automatically performed based on the image processing method. Application to measured JEM signals demonstrated that the automatic feature presented in this study improved the accuracy of JEM analysis and is expected to be efficient for real-time radar non-cooperative target recognition.

Automatic algorithm for estimating the jet engine blade number from the radar target signature of aircraft targets

Radar is a powerful tool for detecting and tracking airborne targets by day and night. Nowadays, noncooperative target recognition (NCTR) is considered an important subject. NCTR means technologies provide high-resolution target signatures and use them to make some decisions about the type of target that has been detected. Clearly, this function is performed with no cooperation from the target concerned, and it may even not be aware that its signature is being measured for recognition purposes [1]. Jet engine modulation (JEM) [2]–[4] generated by the engine rotation in an aircraft has been used for radar-based NCTR purposes, along with high-resolution range profile and inverse synthetic aperture radar (ISAR) [5]–[7]. Recognition by JEM uses micro-Doppler features induced by a rotating jet engine compressor. The micro-Doppler is characterized by a time-dependent Doppler shift that is different from that caused by the target bulk motion.

Effective reconstruction of insufficient JEM signals based on the CS method

One of the major issues in multifunction radar is the allocation of time resources to maximize the ability of the radar. If jet engine modulation (JEM) is analyzed correctly in an insufficient dwell time environment, the remaining time can be allocated for other tasks. This paper presents effective reconstruction of insufficient JEM signals based on compressed sensing (CS) method. The CS method was employed to extract a refined spectrum from insufficient JEM signals. In the process of applying the CS method to JEM signals, we utilized the modified linear programming (LP) standard form of an optimization method. The approach proposed in this paper is significant because it enables reliable estimation in spite of the insufficient JEM signal. Its application to insufficient JEM signals demonstrated that the reconstruction method presented in this paper improved the accuracy of JEM analysis and is expected to be effective from the perspective of radar resource management.

복소 EMD를 이용한 미약한 JEM의 관측 범위에서 JEM 성분의 추출

제트엔진 변조(Jet Engine Modulation: JEM)는 회전하는 제트엔진 터빈으로부터의 전자기 산란에 따른 레이더 신호의 주파수 변조 현상이다. JEM은 표적의 고유한 정보를 제공하여 대표적인 레이더 표적 인식 수단으로 활용되나, JEM 성분이 미약하게 존재하는 레이더 관측 범위에서는 JEM에 의한 레이더 표적 인식 성능이 저하될 수 있다. 이에 본 논문에서는 복소 신호의 경험적인 모드분리법(Complex Empirical Mode Decomposition: CEMD)를 이용하여 레이더 신호를 여러 기본성분인 고유 모드 함수(Intrinsic Mode Function: IMF)로 분리하고, 신호의 이심률을 기반으로 이들 IMF를 조합하는 근거를 제공하여 JEM 성분을 추출하는 기법을 제시한다. 다양한 신호에 대한 적용 결과를 통하여 제안된 기법이 JEM의 명확성을 개선하는 한편, JEM 해석의 유효 관측 범위를 확장시킬 수 있음을 입증하였다. Jet engine modulation(JEM) is a frequency modulation phenomenon of the radar signal induced by electromagnetic scattering from a rotating jet engine turbine. Although JEM can be used as a representative radar target recognition method by providing unique information on the target, its recognition performance may be degraded in the observation range of weakly present JEM. Hence, this paper presents a method for extracting the JEM component by decomposing the radar signal into intrisic mode functions(IMFs) via complex empirical mode decomposition(CEMD) and by combining them based on signal eccentricity. Its application to various signals demonstrated that the proposed method improved the clarity of JEM analysis and could extend the effective observation range of JEM.

항공기 표적의 레이더 반사 신호에서 제트엔진 정보를 추출하기 위한 자동화 알고리즘

제트엔진변조(Jet Engine Modulation: JEM) 식별기법은 제트엔진의 주기적인 회전에 의해 변조되는 레이더 반사 신호로부터 제트엔진의 종류를 식별하는 기법이다. 본 논문에서는 JEM의 새로운 접근법으로서, 제트엔진의 특성을 추출하기 위한 자동화 알고리즘을 제안한다. 먼저 복소 신호의 경험적인 모드 분리법(Complex Empirical Mode Decomposition: CEMD)을 거친 JEM 신호의 자기상관도로부터 제트엔진의 회전 주기를 산출한다. 그 후 DM(Divisor-Multiplier) 규칙 및 'Scoring' 개념을 JEM 스펙트럼 해석에 도입하여 최종적인 날개개수를 추정한다. 시뮬레이션 및 측정 JEM 신호로의 적용 결과를 통해 제안된 알고리즘이 정확하고 자동적인 제트엔진 정보 추출에 효과적임을 입증하였다. Jet engine modulation(JEM) is a technique used to identify the jet engine type from the radar target signature modulated by periodic rotation of the jet engine mounted on the aircraft target. As a new approach of JEM, this paper proposes an automatic algorithm for extracting the jet engine information. First, the rotation period of the jet engine is yielded from auto-correlation of the JEM signal preprocessed by complex empirical mode decomposition(CEMD). Then, the final blade number is estimated by introducing the DM(Divisor-Multiplier) rule and the 'Scoring' concept into JEM spectral analysis. Application results of the simulated and measured JEM signals demonstrated that the proposed algorithm is effective in accurate and automatic extraction of the jet engine information.

제트 엔진 변조신호에서 주파수 마스킹을 이용한 표적의 특징 추출 및 식별

Abstract This paper deals with the method to classify the aircraft target by analyzing its JEM signal. We propose the method to classify the engine model by analyzing JEM spectrum using the harmonic frequency mask generated from the blade information of jet engine. The proposed method does not need the complicated logic algorithm to find the chopping frequency in each rotor stage and the pre-simulated engine spectrum DB used in the previous methods. In addition, we propose the method to estimate the precise spool rate and it reduces the error in estimating the number of blades or in calculating the harmonic frequency of frequency mask.Key words: JEM(Jet Engine Modulation), Target Classification, RTS(Radar Target Signature) klmnop(Agency for Defense Development)Manuscript received November 5, 2013 ; Revised January 2, 2014 ; Accepted January 24, 2014. (ID No. 20131105-109)Corresponding Author: Si-Ho Kim (e-mail: sihokim@add.re.kr) q. 서 론 r s(radar)`t3 uVevOw 'l xG, yzIb{ Dd() |R}~Zd*e, %z2€- O7 XY Z[X

Extraction of jet engine modulation component weakly present in measured signals for enhanced radar target recognition

This study presents a method for extracting the jet engine modulation (JEM) component weakly present in the radar received signal. First, the analytic form of the complicated measured signal is decomposed into intrinsic mode functions (IMFs) via complex empirical mode decomposition (CEMD). Then, the extracted IMFs are combined to reconstruct the JEM component based on the signal eccentricity, which serves as assessing the JEM micro-Doppler contribution to the signal. Applying these techniques to the measured raw signals demonstrate that the proposed extraction method significantly improves the clarity of the JEM analysis and is expected to be useful for enhanced radar target recognition.

AUTOMATIC TARGET RECOGNITION USING JET ENGINE MODULATION AND TIME-FREQUENCY TRANSFORM

We propose a method to recognize targets by using the signature of jet engine modulation (JEM) generated by the rotating blades in jet engines. The method combines time-frequency transform, 2-dimensional (2D) principal component analysis, and a nearest-neighbor classifier. In simulations using five propellers composed of isotropic point scatterers, the proposed method was insensitive to signal-tonoise SNR variation; this insensitivity was a result of the effective 2D time-frequency feature and the noise suppression by the matched filter. In simulations using a reduced training database, the result was most sensitive to variation in the rotation velocity of the blades.

Robust Classification Scheme for Airplane Targets With Low Resolution Radar Based on EMD-CLEAN Feature Extraction Method

A novel classification scheme is proposed to categorize airplane targets into three kinds, i.e., turbojet aircraft, prop aircraft, and helicopter based on the jet engine modulation (JEM) characteristics of their low resolution radar echoes. From the pattern classification viewpoint, the low-dimensional feature vector is extracted via a two-step feature extraction algorithm based on empirical mode decomposition method and CLEAN technique. The feature extraction method can separate the fuselage component and JEM component in the target echo, and sufficiently utilize the information within or between the two components to extract the discriminative features for the three kinds of aircrafts. In addition, because the noise level of a test sample is usually different from those of the training samples in the real application, a simple and efficient preprocessing method is proposed for the classification stage to denoise the received test sample. Experimental results based on the simulated and measured data are presented, including the performance analysis for different dwell time, pulse repeat frequency (PRF) or signal-noise-ratio (SNR) cases and comparison with some related methods.

Analysis of jet engine modulation effect with extended Hilbert‐Huang transform

The jet engine modulation (JEM) effect is widely used for radar target identification. The Hilbert-Huang transform (HHT), a high-resolution time-frequency technique, is extended in order to make an effective JEM analysis. The simulation and measurement results show that the extended HHT not only extracts jet engine features but also definitely exhibits sinusoidal waveforms of rotation-induced micro-Doppler signatures. It also has better applicability for signatures corrupted by noise than other conventional time-frequency techniques.