Target Range Profile Research Articles

Reinforcement Learning Controlled Metasurface to Generate False Target Range Profile

Reinforcement Learning Controlled Metasurface to Generate False Target Range Profile

Focusing Algorithm of Range Profile for Plasma-Sheath-Enveloped Target

In this paper, a one-dimensional (1-D) range profile of the hypersonic target enveloped by a plasma sheath is investigated. Firstly, the non-uniform property of the plasma sheath is studied and its impact on the wideband electromagnetic (EM) wave is analyzed. A wideband radar echo model for the plasma-sheath-enveloped hypersonic target is constructed. Then, by exploiting the relationship among the incident depth, reflection intensity, and plasma velocity, it reveals that distinct scatter points in various areas of the target will suffer from varying reflection intensity and coupled velocity, leading to severe defocusing in the range profile. To tackle this issue, a novel focusing algorithm combing the Fractional Fourier Transform (FRFT) with the CLEAN technique is developed, which independently calculates the coupled plasma velocity and compensates for the phase error via a series of iterative procedures. Finally, the influence of the plasma sheath on the 1-D range profile and the effectiveness of the proposed focusing algorithm are validated through simulations.

Adaptive soft threshold transformer for radar high‐resolution range profile target recognition

AbstractRadar High‐Resolution Range Profile (HRRP) has great potential for target recognition because it can provide target structural information. Existing work commonly applies deep learning to extract deep features from HRRPs and achieve impressive recognition performance. However, most approaches are unable to distinguish between the target and non‐target regions in the feature extraction process and do not fully consider the impact of background noise, which is harmful to recognition, especially at low signal‐to‐noise ratios (SNR). To tackle these problems, the authors propose a radar HRRP target recognition framework termed Adaptive Soft Threshold Transformer (ASTT), which is composed of a patch embedding (PE) layer, ASTT blocks, and Discrete Wavelet Patch Merging (DWPM) layers. Given the limited semantic information of individual range cells, the PE layer integrates nearby isolated range cells into semantically explicit target structure patches. Thanks to its convolutional layer and attention mechanism, the ASTT blocks assign a weight to each patch to locate the target areas in the HRRP while capturing local features and constructing sequence correlations. Moreover, the ASTT block efficiently filters noise features in combination with a soft threshold function to further enhance the recognition performance at low SNR, where the threshold is adaptively determined. Utilising the reversibility of the discrete wavelet transform, the DWPM layer efficiently eliminates the loss of valuable information during the pooling process. Experiments based on simulated and measured datasets show that the proposed method has excellent target recognition performance, noise robustness, and small‐scale range shift robustness.

Group-Fusion One-Dimensional Convolutional Neural Network for Ballistic Target High-Resolution Range Profile Recognition with Layer-Wise Auxiliary Classifiers

Ballistic missile defense systems require accurate target recognition technology. Effective feature extraction is crucial for this purpose. The deep convolutional neural network (CNN) has proven to be an effective method for recognizing high-resolution range profiles (HRRPs) of ballistic targets. It excels in perceiving local features and extracting robust features. However, the standard CNN's fully connected manner results in high computational complexity, which is unsuitable for deployment in real-time missile defense systems with stringent performance requirements. To address the issue of computational complexity in HRRP recognition based on the standard one-dimensional CNN (1DCNN), we propose a lightweight network called group-fusion 1DCNN with layer-wise auxiliary classifiers (GFAC-1DCNN). GFAC-1DCNN employs group convolution (G-Conv) instead of standard convolution to effectively reduce model complexity. Simply using G-Conv, however, may decrease model recognition accuracy due to the lack of information flow between feature maps generated by each G-Conv. To overcome this limitation, we introduce a linear fusion layer to combine the output features of G-Convs, thereby improving recognition accuracy. Additionally, besides the main classifier at the deepest layer, we construct layer-wise auxiliary classifiers for different hierarchical features. The results from all classifiers are then fused for comprehensive target recognition. Extensive experiments demonstrate that GFAC-1DCNN with such simple and effective techniques achieves higher overall testing accuracy than state-of-the-art ballistic target HRRP recognition models, while significantly reducing model complexity. It also exhibits a higher recall rate for warhead recognition compared to other methods. Based on these compelling results, we believe this work is valuable in reducing workload and enhancing missile interception rates in missile defense systems.

Target Measurement in Radio Frequency Simulation Using Code-Interrupted Transmitting and Receiving of Pulse Signals

The interrupted transmitting and receiving (ITR) has been utilized in radio frequency simulation to conduct target measurement with pulse signal. Because the ITR results in partly missing of pulse signal, the spectrum aliasing occurs and the target range profile is difficult to be obtained. Therefore, frequency domain filtering and sparse reconstruction are proposed to recover the target range profile. However, frequency domain filtering is ineffective when wideband signal is utilized. The calculation of sparse reconstruction is complex and the reconstruction performance may be poor for some cases of ITR parameters. To solve this problem, the code-interrupted transmitting and receiving (code-ITR) of pulse signal is proposed in this paper. Firstly, the ITR sequences is coded according the duty ratio of each sub-pulse. Secondly, the echo combination and cancellation are processed to eliminate the false peaks in the range profile of ITR echo. By using the code-ITR and echo processing method, the range profile can be obtained for target measurement. Simulations and experiments are performed to verify the effectiveness of the proposed method.

Near-Field IPO for Analysis of EM Scattering from Multiple Hybrid Dielectric and Conductor Target and High Resolution Range Profiles

Aiming at improving the accuracy and efficiency of scattering information from multiple targets in near-field regions, this paper proposes a near-field iterative physical optics (IPO) method based on a modified near-field Green’s function for the composite electromagnetic scattering analysis of multiple hybrid dielectric and conductor targets. According to the electric field and magnetic field integral equation, the electric and magnetic current were updated utilizing the Jacobi iteration method. Then, by introducing an expansion center lying in the neighborhood of the source point, Green’s function was modified for near-field scattering between multiple hybrid dielectric and conductor targets. To accelerate the implementation of the procedure, the multilevel fast multipole method, the fast far-field approximation, and parallel multicore programming were introduced. Numerical results indicate that there is good agreement between the results calculated by the near-field IPO method and MLFMM solver in commercial software FEKO while significantly reducing the computational burden. To fully exploit the scattering information, the high resolution range profiles (HRRP) of different targets under different conditions were analyzed, which can be further applied for automatic target detection and recognition.

Complex Mutual Information-Based Least-Dependent Component Analysis for ISAR Imaging of Multiple Targets in a Formation Flight

This article proposes a signal decomposition-based approach for inverse synthetic aperture radar (ISAR) imaging of multiple targets in a formation. The procedure of the proposed framework is divided into three steps. The first step is to preprocess the radar echoes received by the spatial multichannel radar via pulse compression. The second step is signal decomposition using the complex-valued mutual information-based least-dependent component analysis proposed in this article. In this step, the range profiles (RPs) of multiple targets are separated into individual RPs to generate an ISAR image for each target. The third step is range-Doppler imaging using the separated RPs. As compared to the conventional methods, the proposed method decomposes the superimposed radar echoes at the raw signal level. Therefore, even if multiple targets overlap in the ISAR image, or RP histories are unaligned owing to the change in the relative positions between multiple targets, a well-focused ISAR image can still be generated. Simulation results using three targets composed of point scatterer centers verify that the proposed method can effectively segment three targets closely flying in a formation.

Waveform design and processing method of pulse signal in anechoic chamber measurement

The application of pulse radar in anechoic chamber measurement is a new direction to obtain characteristics of targets. However, due to the limited size of anechoic chamber, the transmitted signal and its echo are coupled so that the target range profile can not be accurately obtained. Furthermore, the position and intensity of target scattering points can not be measured. To solve this problem, the waveform design and processing method of pulse signal is proposed in this paper. Firstly, the waveform is designed by amplitude modulation to solve the coupling between transmitted signal and its echo. Secondly, for the designed waveform, the echo processing method based on decomposition, combination, and cancellation is provided to obtain the target range profile so that the target measurement can be conducted in anechoic chamber. Considering the actual situation of multipath clutter in anechoic chamber, simulations and experiments are conducted, and the results demonstrate the validity of the proposed method.

Remote Vital Sign Monitoring With Reduced Random Body Swaying Motion Using Heartbeat Template and Wavelet Transform Based on Constellation Diagrams

Remote vital signs monitoring using millimeter-wave (mmWave) sensors has gained lots of attention because of their contactless, portable advantages. However, their received signals are more sensitive to random body motions (RBM) which degrades the accuracy of heart rate (HR) detection. To overcome this challenge, multi-input multi-output (MIMO) configuration can be used to reduce RBM's impact as each channel has different points of view with respect to the subject under test (SUT). Here we propose the use of a Frequency Modulated Continuous Wave (FMCW) radar from Texas Instruments (TI) at 77 GHz to collect data from its 192-channel configuration. Since vital sign information extracted using Arctangent Demodulation (AD) could be corrupted by either RBM or respiratory harmonics, a method is needed to minimize such effects. Hence, we develop an algorithm where a Heartbeat Template (HBT) is extruded based on the Constellation Diagram that shows the Quadrature signals from the target's range profile. The HBT is then used to design an adapted-wavelet for Continuous Wavelet Transform (CWT) to magnify the heartbeat signals. Under circumstances where RBM overwhelms the heartbeat signal that the HBT cannot completely reduce its effects, a spectral-based HR selection method is also developed to estimate the HR. By employing the proposed methods, we have reduced mean-error <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mu _e}$</tex-math></inline-formula> of HR estimation significantly from 9 bpm to less than 2 bpm.

Multi‐scale group‐fusion convolutional neural network for high‐resolution range profile target recognition

Convolution neural networks (CNNs) represent one of the workhorses of artificial intelligence applications. As a typical artificial intelligence application, a high-resolution range profile (HRRP) target recognition method based on CNNs has aroused a lot of research interest. Most CNNs use a relatively small and single-scale convolution kernel size to control the number of parameters and computational complexity, but recent studies indicate that CNNs with a small kernel size cannot extract enough spatial information, which hurts the recognition performance. Aiming at this problem, this paper proposes a multi-scale group-fusion one-dimensional convolution neural network (MSGF-1D-CNN) for HRRP target recognition. MSGF-1D-CNN utilises multi-scale group one-dimensional convolution (MSG 1D-Conv) and point-wise convolution (PW-Conv) to replace the standard convolution. Multi-scale group one-dimensional convolution can significantly reduce complexity and capture the information of targets within HRRP in different levels of detail to enhance feature extraction, while PW-Conv can realise the fusion of multi-scale features to help boosting recognition performance. Experiments on five mid-course ballistic targets in the HRRP dataset show that MSGF-1D-CNN has superior recognition performance, and the parameter number of the model is reduced by more than 2.4 times than standard 1D-CNN. Furthermore, MSGF-1D-CNN shows better performance on fine-grained HRRP target recognition and anti-noise robustness in most cases.

ISAR Imaging Analysis of a Hypersonic Vehicle Covered With Plasma Sheath

In this article, a hypersonic target electromagnetic (EM) scattering echo model combined with the inhomogeneous zonal medium model (IZMM) and the classical scattering center model (SCM) is proposed with a distributed satelliteborne array radar as the detection platform. A parallel physical optics (PO) method is used for multiview inverse synthetic aperture radar (ISAR) imaging of a moving hypersonic target covered with plasma sheath based on the analysis of high-resolution range profile in the S-X ultrawideband range, reconstructing 2-D EM scattering echo data (the target) and motion compensation. The results show that the surface of the inhomogeneous plasma sheath flow field is an excitation layer with random and irregular fluctuation characteristics, which increases the false scattering centroid of the 1-D range profile of the hypersonic target and can interfere with and disrupt the radar localization of the target along the radial direction. In addition, shallow scattering of EM waves occurs in the plasma sheath, and the average signal intensity of the target can gradually reduce from 0.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times \,\,10^{-5}$ </tex-math></inline-formula> at 60 km and 20 Ma to 0.1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times \,\,10^{-5}$ </tex-math></inline-formula> at 30 km and 20 Ma, with a fivefold weakening of the overall scattered echo signal. In particular, the faster the hypersonic target travels at 30-km altitude, the weaker the imaged scattered echo signal becomes, with the average intensity of the imaged signal weakening by approximately threefold from 15 to 25 Ma. This study provides considerable technical support and data assurance to establish a synthetic aperture radar (SAR) automatic target recognition (ATR) database, and the findings of this study can be used as a reference for fine-structure feature analysis of hypersonic targets for feature extraction and the classification and identification of targets.

Radar High-Resolution Range Profile Target Recognition by the Dual Parallel Sequence Network Model

Using traditional neural network algorithms to adapt to high-resolution range profile (HRRP) target recognition is a complex problem in the current radar target recognition field. Under the premise of in-depth analysis of the long short-term memory (LSTM) network structure and algorithm, this study uses an attention model to extract data from the sequence. We build a dual parallel sequence network model for rapid classification and recognition and to effectively improve the initial LSTM network structure while reducing network layers. Through demonstration by designing control experiments, the target recognition performance of HRRP is demonstrated. The experimental results show that the bidirectional long short-term memory (BiLSTM) algorithm has obvious advantages over the template matching method and initial LSTM networks. The improved BiLSTM algorithm proposed in this study has significantly improved the radar HRRP target recognition accuracy, which enhanced the effectiveness of the improved algorithm.

Design and experimental research of precession target micro-Doppler measurement using pulse signal in anechoic chamber

Target characteristics can be obtained in anechoic chamber by the traditional static measurement. As the dynamic characteristic of precession target becomes more and more complex, it is urgent to perform the dynamic measurement of precession target with pulse radar signal. However, the transmitted pulse and target echo may be coupled because the anechoic chamber is range limited. To solve the problem, the pulse signal is divided into several short pulses by interrupted transmitting and receiving (ITR). According to the sparse property of ITR echo, the precession target range profile recovery method is proposed by modelling the matched filter transform matrix. After that, time frequency analysis (TFA) is performed to obtain the micro-Doppler of precession target with the recovered range profile. The whole procedure for precession target micro-Doppler measurement is then provided based on the proposed method. In particular, the dynamic measurement experiments are designed and analyzed in anechoic chamber. The micro-Doppler is obtained precisely which indicates the effectiveness of range profile recovery method.

Multiple radar subbands fusion technique based on generalized likelihood ratio test

The multi-band signal fusion method is a practical and effective technique to improve the range resolution of the radar. This paper proposes a multi-stage approach based on the Generalized Likelihood Ratio (GLR) test for estimating the target range profile using sparse subband measurement data. The proposed subband fusion algorithm estimates, and compensates for the incoherent factors between the subbands, in the first step. In the second step, by combining the data of subbands, the model parameters and the number of scattering centers are estimated based on the Geometrical Theory of Diffraction (GTD) model. Finally, based on the initial values of the model parameters and the incoherent factors obtained in the preceding steps, the GTD model parameters, and the incoherence parameters are re-estimated simultaneously based on the GLR test to improve the parameters’ accuracy. In comparison to the conventional methods, where the incoherence parameters and the model parameters are independently estimated, the accuracy of the estimation of the parameters in the proposed method has improved with the simultaneous estimation of these two types of parameters. Also, the proposed method does not require prior information about the number of scattering centers. The validity and performance of this algorithm have been investigated with analytical and simulated data.

Radar Target Classification Based on High Resolution Range Profile Segmentation and Ensemble Classification

Radar target range profile possesses advantages in many target recognition problems. Feature extraction techniques could optimize target recognition performance, but conventional methods neglect specific characters of electromagnetic scattering mechanisms in range profiles. This letter introduces a range profile segmentation algorithm, which decomposes target range profile into segments corresponding to particular electromagnetic scattering mechanisms. Segment type identification and weighing algorithms are also developed for ensemble classification strategy. Results indicate that the proposed method could achieve comparable classification accuracy as advanced neural networks but requires far fewer computational resources to guarantee its application flexibility.

Jamming Efficiency Analysis Based on the Range Profile of Target With Chaff

Chaff cloud is widely used in target covering. In this paper, a jamming performance estimation strategy is presented for the elliptical and cubic chaff clouds. The range profiles of the composite models of the plane with the chaff cloud are experimentally accessed in an anechoic chamber using a stepped-frequency waveform measurement system. Then, dimensions, characteristic length, and distributing entropy of the equivalent scattering centers of the plane and the jammer are extracted from the measured range profiles, respectively. Based on these features, support vector machines, naive Bayesian classification, and decision tree classification methods are employed to determine the recognition rate of the target under the chaff jamming, and then determine the jamming efficiency of the two kinds of chaff clouds. Results show that two types of chaff clouds are able to reduce the target recognition rate. Besides, when the radar uses the Euclidean distance and decision tree classification methods to complete the recognition task, a good jamming efficiency can be achieved by releasing the elliptical or the cubic chaff cloud. Meanwhile, a low level of jamming performance outcomes using the support vector machines classification method. It is also found that the chaff cloud released from the side of the plane achieves a better jamming performance compared with the other releasing locations. Finally, the influence of environmental noise on the recognition rate of the plane is studied. Estimating results show that the recognition rates decrease with the increasing of the power level of the noise. When the transmitting power level of the noise reaches 1.2 times the reflected power level of the target, it is quite hard to estimate the jamming performance of two kinds of chaff clouds.

Radar group target recognition based on HRRPs and weighted mean shift clustering

When range high-resolution radar is applied to target recognition, it is quite possible for the high-resolution range profiles (HRRPs) of group targets in a beam to overlap, which reduces the target recognition performance of the radar. In this paper, we propose a group target recognition method based on a weighted mean shift (weighted-MS) clustering method. During the training phase, subtarget features are extracted based on the template database, which is established through simulation or data acquisition, and the features are fed to the support vector machine (SVM) classifier to obtain the classifier parameters. In the test phase, the weighted-MS algorithm is exploited to extract the HRRP of each subtarget. Then, the features of the subtarget HRRP are extracted and used as input in the SVM classifier to be recognized. Compared to the traditional group target recognition method, the proposed method has the advantages of requiring only a small amount of computation, setting parameters automatically, and having no requirement for target motion. The experimental results based on the measured data show that the method proposed in this paper has better recognition performance and is more robust against noise than other recognition methods.

Rotating Target Size and Rotation Vector Estimation in a Distributed Stepped Frequency Radar System

Radar echoes produced by a target contain the target motion information and physical characteristics. In a stepped frequency (SF) signal, a range-Doppler coupling occurs where the movement of the target affects the target range profile. Using this characteristic, in this paper, an algorithm for estimating the size and rotation vector of the rotating target is proposed. For this, we consider a distributed radar system to utilize target-sensor geometry. By executing signature extraction from the high resolution range profiles (HRRPs) on time-range images and two nonlinear least squares (NLS) estimations, the desired parameters are successfully estimated. Through several simulations, the performance of the proposed method is analyzed and the favorable results are obtained.

Sparse Representation for Target Parameter Estimation in CDR-Based Passive Radar

In the China digital radio (CDR)-based passive radar, when the range-Doppler (RD) map is generated by the classical matched filtering method, the target range profile has the mainlobe splitting phenomenon. To solve this problem, we first derive the reason for the target mainlobe splitting from the subcarrier domain. Then, we develop an effective-subcarrier-based complex fast sparse Bayesian learning (ES-CFSBL) algorithm to generate RD map. Finally, simulation and experimental results show that the developed algorithm can effectively reduce sidelobe level and make it easier to detect and track weak targets covered by strong target sidelobes. Besides, the proposed method can realize fractional delay estimation conveniently.

Adversarial attacks on deep-learning-based radar range profile target recognition

Target recognition based on a high-resolution range profile (HRRP) has always been a research hotspot in the radar signal interpretation field. Deep learning has been an important method for HRRP target recognition. However, recent research has shown that optical image target recognition methods based on deep learning are vulnerable to adversarial samples. Whether HRRP target recognition methods based on deep learning can be attacked remains an open question. In this paper, four methods of generating adversarial perturbations are proposed. Algorithm 1 generates the nontargeted fine-grained perturbation based on the binary search method. Algorithm 2 generates the targeted fine-grained perturbation based on the multiple-iteration method. Algorithm 3 generates the nontargeted universal adversarial perturbation (UAP) based on aggregating some fine-grained perturbations. Algorithm 4 generates the targeted universal perturbation based on scaling one fine-grained perturbation. These perturbations are used to generate adversarial samples to attack HRRP target recognition methods based on deep learning under white-box and black-box attacks. The experiments are conducted with actual radar data and show that the HRRP adversarial samples have certain aggressiveness. Therefore, HRRP target recognition methods based on deep learning have potential security risks.