Radar Target Signatures Research Articles

Composite Gaussian pulsed waveform for robust resonance radar signal

The paper assesses the feasibility of forming a composite excitation pulse with a high potential to combat the noise and onset ambiguity when estimating the target resonance behaviour in a radar target signal. The assessment investigates four composite pulse configurations of unified or adaptive setups for the fractional bandwidth and peak weight to find the best setup to enhance the resonance signature robustness. The assessment uses the method pencil function to extract the resonance parameters of the composite time data (coherent) and then to determine the degree of robustness over-extraction onset and range of noise level. Determining the robustness rate requires finding the error between the original excitation frequencies and the extractable resonant frequencies and, second, the similarity between the original and reconstructed pulse waveforms. The qualitative assessments of the robustness merit concluded that the adaptive configuration of peak weight and small adaptive fractional bandwidth outperforms the other arrangements in enhancing the resonance return in radar signature.

Multi-Feature Transformer-Based Learning for Continuous Human Motion Recognition with High Similarity Using mmWave FMCW Radar.

Doppler-radar-based continuous human motion recognition recently has attracted extensive attention, which is a favorable choice for privacy and personal security. Existing results of continuous human motion recognition (CHMR) using mmWave FMCW Radar are not considered the continuous human motion with the high similarity problem. In this paper, we proposed a new CHMR algorithm with the consideration of the high similarity (HS) problem, called as CHMR-HS, by using the modified Transformer-based learning model. As far as we know, this is the first result in the literature to investigate the continuous HMR with the high similarity. To obtain the clear FMCW radar images, the background and target signals of the detected human are separated through the background denoising and the target extraction algorithms. To investigate the effects of the spectral-temporal multi-features with different dimensions, Doppler, range, and angle signatures are extracted as the 2D features and range-Doppler-time and range-angle-time signatures are extracted as the 3D features. The 2D/3D features are trained into the adjusted Transformer-encoder model to distinguish the difference of the high-similarity human motions. The conventional Transformer-decoder model is also re-designed to be Transformer-sequential-decoder model such that Transformer-sequential-decoder model can successfully recognize the continuous human motions with the high similarity. The experimental results show that the accuracy of our proposed CHMR-HS scheme are 95.2% and 94.5% if using 3D and 2D features, the simulation results also illustrates that our CHMR-HS scheme has advantages over existing CHMR schemes.

Multifunctional Scatterometer System for Measuring Physical Oceanographic Parameters Using Range-Doppler FMCW Radar.

A multifunctional scatterometer system and optimized radar signal processing for simultaneous observation of various physical oceanographic parameters are described in this paper. Existing observation methods with microwave remote sensing techniques generally use several separate systems such as scatterometer, altimeter, and Doppler radar for sea surface monitoring, which are inefficient in system operation and cross-analysis of each observation data. To improve this point, we integrated separate measurement functions into a single observation system by adding a measurement function of Doppler frequency to the existing system. So it enables to simultaneously measure the range and polarimetric responses of backscattering as well as movements of the sea surface. Here, the simultaneous measurement function of Doppler frequency was implemented by sampling an FMCW (frequency modulated continuous wave) radar signal as 2D raw data consisting of fast- and slow-time samples, i.e., the range and backscattering of radar target signals are analyzed from the fast-time samples while the Doppler frequency by the radar target’s movement extracts from the slow-time samples. Through the Fourier transformed-based range-Doppler signal process, distance (R), backscattering (σ°), and Doppler frequency (fD) are sequentially extracted from the 2D raw data, and a correlation to the physical oceanographic parameters is analyzed. Operability of the proposed system was examed through total 3 times of field campaigns from June 2017 to August 2020 and the observation data retrieved by the radar measurement data (R, σ°, fD) was also cross-analyzed with in-situ data: e.g., tide, significant wave height, and wind speed and direction. Differences in the comparative results as an observational accuracy are as follows. Tidal level (Root Mean Square Error 0.169 m (R)), significant wave height (RMSE 0.127 m (R), 0.362 m (σ°)), wind speed (RMSE 1.880 m/s (fD), 2.094 m/s (σ°)) and direction (18.84° (fD)).

Superresolution Radar Imaging via Peak Search and Compressed Sensing

Compressed sensing (CS) based imaging technique is considered to be an effective solution for high-resolution radar imaging due to the sparse distribution of the scatterers. Nevertheless, the unoptimized CS based synthetic aperture radar (SAR) or inverse SAR (ISAR) imaging approach may suffer from the computationally intensive problem when apply it to wideband radar signatures of electrical large-scale targets. In this paper, a two-dimensional (2-D) superresolution imaging technique based on peak search and compressed sensing (PS-CS) is presented. A peak search strategy is first developed to solve the problem of high computational complexity of CS based method in scattering parameter estimation. Superresolution imaging result is then achieved by extrapolating the estimated parameters of scattering along with the observing angle dimension and frequency dimension. The numerical and measurement data acquired from different man-made targets are presented to demonstrate the feasibility and usefulness of the proposed technique for supperresolution radar imaging.

3-D electromagnetic-model-based absolute attitude estimation using a deep neural network

ABSTRACT With the improvement of range resolution, wideband radars can achieve not only the range and velocity but also signature information of the target, such as the target RCS and the distribution of the scattering centres. Since radar target signature is closely related to target’s geometric structure and attitude, it is possible to estimate attitude from wideband radar echoes. To this end, we propose a novel end-to-end Radar Target Attitude Estimation Network (RTAENet) to estimate the absolute attitude of the radar target. A difficulty for training the network is that a large dataset with ground truth is required. To solve this issue, we introduce the three-dimensional (3-D) electromagnetic model to generate training samples. Since the RTAENet requires very little feature engineering by hand and can take advantage of increases in the amount of available data, it achieves high accuracy for attitude estimation. Experiments using both data predicted by a high-frequency electromagnetic code and data measured in an anechoic chamber demonstrate the feasibility of the proposed method.

The Fortress Beneath: Ground Penetrating Radar Imaging of the Citadel at Alcatraz: 1. A Guide for Interpretation

Ground-penetrating radar has emerged as a prominent non-destructive evaluation tool for the study of inaccessible subsurface elements of cultural heritage structures. Often of central interest is the desire to image the remains of a pre-existing historic structure that is located directly beneath a more recently built one. The interpretation of GPR images in such cases is usually difficult due to ambiguities caused by the presence of pervasive clutter, environmental noise, and overlapping target signatures. Sites with abundant ground truth allow for more confident interpretations and serve as a useful testbed to assist similar studies at other places, where little or no ground truth is available. This study reports GPR interpretations of structures belonging to the 19th century Citadel beneath the main prison cellhouse at Alcatraz. At this site, lidar scans, direct observations, and historical documents are available to facilitate identification of radar target signatures. A general interpretation of the acquired radargrams is made in this paper, while the companion paper presents more advanced analysis of target signatures based on curvelet image processing. This study points to the development of a radar facies classification scheme that is specific to cultural heritage investigations.

Analytical Model and Real-Time Calculation of Target Echo Signals on Wideband LFM Radar

In wideband radar system testing and performance evaluation, target echo simulation should simultaneously meet real-time and high-precision requirements. In this paper, we focus on the wideband linear frequency modulated (LFM) radar signal, derive the echo signal time-domain analytical expression based on the multiscattering center model and propose a high-precision real-time echo simulation calculation method. Regarding the low model reliability and poor versatility problems in the current wideband radar target calculation, the analytical model proposed here truly reflects the inner-pulse modulation characteristics of the radar transmit signal and the wideband electromagnetic scattering characteristics of the target. Current wideband radar target signal calculation accuracy and real-time performance are also difficult to balance. Considering this problem, the wideband radar target echo analytical model proposed in this paper avoids the large amount of calculations required for the full numerical calculation and effectively improves the calculation accuracy and real-time performance of wideband radar target echo signal calculations without reducing calculation accuracy. The real-time calculation method involves elaborate amplitude-phase control. Simulation analysis and experimental verification confirmed the reliability and validity of these results. These research results have important theoretical significance and practical application value in the field of hardware-in-the-loop simulation.

Methods and algorithms of angular estimation with super-resolution for aviation airborne radar systems

In the practical application of aircraft, there may be situations when it is not possible to resolve radar target signals in the measurement channels of range, speed and angular coordinates in the on-board radar system. In these situations, it is advisable to use angular estimation procedures with super-resolution in the onboard radar system. Various methods and algorithms of angular estimation with super-resolution are known, each of which has its own advantages and disadvantages. At the same time, there is no information in the available scientific and technical literature about the algorithms of angular estimation with super-resolution used in the onboard radar system, knowledge of which is necessary when designing promising onboard radar systems. The article systematizes and analyzes the methods and algorithms of angular estimation with super-resolution. The classification of methods of angular estimation with superresolution is given. The generalized information about the known one-dimensional and twodimensional methods and algorithms of angular estimation is given. The algorithms proposed for use in flat antenna arrays, concentric ring antenna arrays, ring antenna arrays, round four-quadrant antenna arrays, flat antenna arrays with a total output signal are considered. It is noted that the features of the construction of radar homing heads include a reduction in the total number of information processing channels achieved by combining antenna elements into sublattices, the output signals of which are used to estimate the angular coordinates of radiation sources. The specificity of such antenna arrays is that the antenna sublattices have radiation patterns that are narrower than those of weakly directed antenna elements. In addition, the distance between the antenna modules can significantly exceed the distance between the antenna elements in a conventional antenna array and at the same time significantly exceed the wavelength size. To determine the possibilities of applying in practice the algorithms of angular two-dimensional estimation with super-resolution considered in the article in the onboard radar system, it is necessary to conduct special studies.

Compressing bistatic SAR target signatures with sparse‐limited persistence scattering models

Here, the authors study the modelling of bistatic synthetic aperture radar (SAR) target signatures. They focus on building concise models inspired by physical phenomenology that explain target reflectivity over a wide range of bistatic angles and target pose. A regularised inversion method is proposed that decomposes the target into a set of scattering centres with limited persistence by utilising the model-based sparsity of scattering coefficients. The resulting sparse model can be used for online estimation of target signatures at much faster speeds than simulation or database approaches. Based on the prior work on parametric models derived for canonical reflectors, the authors hypothesise that the scattering coefficient as a function of the viewing angle is embedded in a low-dimensional subspace spanned by a set of functions or dictionary atoms that have a support, which is concentrated in the viewing angle domain. Exploiting the differentiability of the observation model in the scattering centre locations and the parameters associated with the basic functions, the authors propose a method to estimate the scattering centre locations in the continuum avoiding gridding errors. Using numerical simulations, they verify that the proposed method obtains a succinct representation of these objects using a sufficiently small set of scattering centres.

Adaptive Radar Signal Detection in Autoregressive Interference using Kalman-based Filters

This paper deals with the adaptive detection of radar target signal with unknown amplitude embedded in Gaussian interference which has been modelled as an AR process. Considering such model for the interference decreases the number of parameters that must be estimated and therefore less or even no secondary data is needed to obtain a detector with desired performance. Herein the detection is based on only the primary data. The authors resorting to the modern Kalman filtering technique develop the conventional GLRT-based detection in the presence of AR interference and propose two new detectors; AREKF based on extended Kalman filter and ARUKF based on unscented Kalman filter. The performance assessment conducted by Monte Carlo simulation compares the proposed detectors with the existing detectors based on generalised likelihood ratio test and Kalman filter. The results show that the ARUKF detector significantly has better detection performance than that of other detectors for the low number of primary data and high signal to noise ratio (SNR).

Short-Time State-Space Method for Micro-Doppler Identification of Walking Subject Using UWB Impulse Doppler Radar

In this paper, ultra-wideband (UWB) Doppler radar signatures from walking human subjects are processed with the state-space method (SSM) to analyze compressed radar echoes for the first time. A new mathematical model is proposed to detect the human walking strides using UWB radar, while micro-Doppler ( $\mu $ -D) features are extracted for gait analysis using short-time SSM (STSSM). To distinguish $\mu $ -D signatures of different subjects’ body parts, the SSM used for the characterization of radar target signatures and sensor fusion is applied on a sliding short-time window to enhance the resolution of feature extraction from data collected on a dismount. This method of the application of SSM to sliding short-time data is validated with a full-wave electromagnetic (EM) simulation of a walking subject using the Boulic model that represents the human kinematics. An EM scattering model is then utilized to compare the performance of a short-time Fourier transform with STSSM. Experimental results show that STSSM can be successfully applied to identify multiple $\mu $ -D trajectories in real experimental data, thus demonstrating the capability to positively identify human motions (right foot, left foot, and torso) even in a low signal-to-noise ratio environment.

Aspect invariant features for radar target recognition

In this study, the authors introduce a novel method of target feature extraction that in one form attempts to reduce variability inherent in target signatures and in a second form attempts to exploit the variability. It is well known that radar target signatures can be subject to significant fluctuations as a function of viewing geometry and that this behaviour severely degrades the performance of automatic target recognition algorithms. Here, they propose one variability reduction technique which groups strong scatterers in target signatures. A second method exploits variability by using the difference between two reduced resolution images to extract target angular stability information. In both cases, a grid cell structure enables extraction of these relatively stable features from synthetic aperture radar images with low computational complexity. Results, using measured target data taken from the MSTAR dataset, demonstrate that the proposed method of selecting feature vectors significantly improve the overall classification performance. A correct classification rate of 96% is reached in testing.

基于目标波形模型的多脉冲激光雷达目标信号模拟

基于目标波形模型的多脉冲激光雷达目标信号模拟

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.

Robust statistical recognition and reconstruction scheme based on hierarchical Bayesian learning of HRR radar target signal

A hierarchical Bayesian model is developed to characterize the complex-valued high range resolution (HRR) radar target signal, motivated by the problem of radar automatic target recognition (RATR) robust to low signal-to-noise ratio (SNR) or narrowband interference. Here we assume a sparseness-promoting prior on the complex echoes and a Markov dependency for the location of the dominant scattering center between consecutive HRR signals. The number of the dominant scattering centers can be automatically determined, while the posterior distributions of their complex coefficients and locations can be inferred via such Bayesian model. In the training stage, based on the proposed Bayesian model, the statistical aspect-frame template can be learned for HRR complex training samples from each target-aspect sector under high SNR and without any interference. Considering the low SNR or narrowband interference problem for a test sample, the aspect-frame templates can be updated to make a robust recognition decision for the noised and interfered test sample. Simultaneously, the test sample can be denoised and recovered via the analytically posterior estimation for the reconstruction, which is referred to as the statistical compressive sensing (CS) inversion. In contrast to the traditional CS methods that only utilize the underlying sparse property of a measurement (here a measurement is a test sample), our statistical CS inversion also exploits the statistical information of the training samples, which are under high SNR and without any interference. Therefore, the better recognition and reconstruction performances can be obtained via our method. Efficient inference is performed via variational Bayesian (VB) for the proposed Bayesian model. To validate the formulation, we present our experimental results on the measured HRR dataset, with comparisons to some state-of-the-art methods.

Other applications of medical microwaves – Breast tumour classification

This talk addresses the development of imaging techniques for the early detection of breast cancer, based on Ultra Wideband (UWB) radar, a promising emerging technology that exploits the dielectric contrast between normal and tumour tissues at microwave frequencies. Of particular interest in this work are issues related to techniques for classification of potential breast tumours into benign and malignant. This is particularly important given the results from recent studies of the dielectric properties of breast and tumour tissue, which have found that strong similarities exist between the dielectric properties of malignant, benign and normal fibroglandular breast tissue. This creates a more challenging imaging scenario and motivates the development of enhanced signal processing techniques for UWB imaging systems.Tumour growth and development patterns are modelled using Gaussian Random Spheres, using four discrete sizes and four different shapes. Feature extraction methods including Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Discrete Wavelet Transform (DWT), are used to extract the most relevant features from the detailed Radar Target Signatures of the tumours, which are then classified with a number of different classification techniques: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machines (SVM). In addition to these techniques, a number of different multi-stage classification architectures are considered. The feature extraction and classification algorithms are evaluated for both homogeneous and heterogeneous breast tissue models, for a range of different tumour sizes and shapes.Also, the first experimental results using a pre-clinical UWB prototype imaging system for tumour classification based on the shape of tumours. A database of benign and malignant tumour phantoms was created using dielectrically–representative tissue-mimicking material. Classification of benign and malignant tumour models of the experimental data was completed with Linear Discriminant Analysis, Quadratic Discriminant Analysis and Support Vector Machines classifiers.

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

Investigation of Range Profiles from a Simplified Ship on Rough Sea Surface and Its Multipath Imaging Mechanisms

The range profiles of a two-dimension (2 D) perfect electric conductor (PEC) ship on a wind-driven rough sea surface are derived by performing an inverse discrete Fourier transform (IDFT) on the wide band backscattered field. The rough sea surface is assuming to be a PEC surface. The back scattered field is computed based on EM numerical simulation when the frequencies are sampled between 100 MHz and 700 MHz. Considering the strong coupling interactions between the ship and sea, the complicated multipath effect to the range profile characteristics is fully analyzed based on the multipath imaging mechanisms. The coupling mechanisms could be explained by means of ray theory prediction and numerical extraction of the coupling currents. The comparison of the range profile locations between ray theory prediction and surface current simulation is implemented and analyzed in this paper. Finally, the influence of different sea states on the radar target signatures has been examined and discussed.

Coherent Radar Target Detection in Heavy-Tailed Compound-Gaussian Clutter

This paper deals with the problem of detecting a radar target signal against correlated non-Gaussian clutter, which is modeled by the compound-Gaussian distribution. We prove that if the texture of compound-Gaussian clutter is modeled by an inverse-gamma distribution, the optimum detector is the optimum Gaussian matched filter detector compared to a data-dependent threshold that varies linearly with a quadratic statistic of the data. We call this optimum detector a linear-threshold detector (LTD). Then, we show that the compound-Gaussian model presented here varies parametrically from the Gaussian clutter model to a clutter model whose tails are evidently heavier than any K-distribution model. Moreover, we show that the generalized likelihood ratio test (GLRT), which is a popular suboptimum detector because of its constant false-alarm rate (CFAR) property, is an optimum detector for our clutter model in the limit as the tails get extremely heavy. The GLRT-LTD is tested against simulated high-resolution sea clutter data to investigate the dependence of its performance on the various clutter parameters.