Cone-shaped Target Research Articles

Super-Resolution Micro-Range Curve Extraction for Precession Cone-Shaped Targets Based on Multidimensional Information

Super-Resolution Micro-Range Curve Extraction for Precession Cone-Shaped Targets Based on Multidimensional Information

Space Targets with Micro-Motion Classification Using Complex-Valued GAN and Kinematically Sifted Methods

Space target classification based on micro-motion characteristics has become a subject of great interest in the field of radar, particularly when using deep learning techniques. However, in practical applications, the ability of deep learning is hampered by the available radar datasets. As a result, obtaining a sufficient amount of the training dataset is a daunting challenge. To address this issue, this paper presents a novel framework for space target classification, consisting of three distinct modules: dataset generation, the kinematically sifted module, and classification. Initially, the micro-motion model of cone-shaped space targets is constructed to analyze target characteristics. Subsequently, the dataset generation module employs a complex-valued generative adversarial network (CV-GAN) to generate a large number of time-range maps. These maps serve as the foundation for training the subsequent modules. Next, the kinematically sifted module is introduced to eliminate images that do not align with the micro-motion characteristics of space targets. By filtering out incompatible images, the module ensures that only relevant and accurate dataset is utilized for further analysis. Finally, the classification model is constructed using complex-valued parallel blocks (CV-PB) to extract valuable information from the target. Experimental results validate the effectiveness of the proposed framework in space micro-motion target classification. The main contribution of the framework is to generate a sufficient amount of high-quality training data that conforms to motion characteristics, and to achieve accurate classification of space targets based on their micro-motion signatures. This breakthrough has significant implications for various applications in space target classification.

A Long-Term Recurrent Convolutional Network for SNR Estimation of Cone-Shaped Target

Signal-to-Noise Ratio (SNR) is an important prior information for many micro-motion echo processing approaches and techniques. Estimating SNR in advance can effectively enhance the performance of such technologies. This letter proposes a Long Term Recurrent Convolutional Network (LRCN) based SNR estimation method for cone-shaped target. First, mathematical expression of the echo is derived by studying the cone-shaped target micro-motion model, and hence the characteristics of the echo and its Short-Time Fourier Transform (STFT) are analyzed. Second, the motivation for the proposed are analyzed, and the echo is converted as an time-frequency graph (TFG) with STFT, so that an estimation technique based on LRCN (a hybrid of convolutional neural network (CNN) and recurrent neural networks (RNN)) is designed to estimate the SNR. Experiments indicate that the proposed method outperforms other already existing methods with better accuracy and robustness.

Photonics radar based LSS targets’ postures’ m-D and cadence frequency imaging using empirical wavelet transform technique

ABSTRACT Developing a photonics radar to detect the combined multiple simultaneous-behaviours/postures of low-slow-small (LSS) aerial targets and appropriately extracting their Doppler profile have become significant to enable air surveillance/security and guidance for unmanned aviation. A CW photonic radar of 5.3 GHz, and an empirical wavelet transform (EWT) based radar signal processing algorithm are developed and their sensing/measurement capabilities are tested with representative LSS target models: two/three blades propeller systems, a warhead-like cone-shaped target and a bionic bird. Open field experiments with different simultaneous-behaviours of these LSS targets are conducted, and performance validation of radar, in terms of micro-Doppler signature extractions, cadence frequency imaging and targets' behavioural parameters measurement, is analysed, quantitatively investigated and the results are presented. All the measurement results keep an average of 99% of agreement with the standard measurement values that evidence the compatibility of developed photonics radar for precision sensing of multiple simultaneous-behaviours/postures of LSS aerial targets.

Micro-Doppler Extraction for Cone-Shaped Target Using Constrained Nonnegative Matrix Factorization

Micro-Doppler (MD) extraction is the precondition for target classification based on micro-motion. This letter proposes a method for MD extraction based on constrained nonnegative matrix factorization (NMF). The proposed algorithm is applied to the time-frequency spectrogram of the cone-shaped precession target. Taking the sparsity constraint, temporal continuity constraint and approximate orthogonality constraint into consideration, the basic NMF is improved to match the MD separation problem, and thus the time-frequency spectrogram of each scattering center is reconstructed by deriving the basis matrix and coefficient matrix. Morphological processing, valid frequency extraction and MD curve fitting are conducted on the reconstructed spectrograms to extract the MD accurately. Experiment results show that the proposed approach outperforms other algorithms with better accuracy and robustness.

High-resolution imaging and micro-motion feature extraction of space multiple targets

In radar applications, different micro-motion forms can be used as the basis of target recognition. However, the radar echo signal of multiple spatial targets is overlapping in the time-frequency and time-range domains, which increases the difficulty of micro-motion feature extraction. In this paper, a high-resolution imaging and micro-motion feature extraction framework based on a multiple joint-domain radar tool is proposed to address this mixed signal. First, an accurate and suitable micro-motion model of cone-shaped space multiple targets is built. Then, the two-dimensional adaptive regularized smoothed L0 norm algorithm (2D AReSL0) based on sparse reconstruction is formulated to directly reconstruct the inverse synthetic aperture radar (ISAR) image. Additionally, the range-frequency-time radar data cube can be extracted from the ISAR movie by the CLEAN algorithm. To solve the scattering point association problem in the radar data cube, a 3D segmentation Viterbi algorithm is designed to extract the micro-motion features. Finally, simulation and experiment results demonstrate the effectiveness of the proposed framework.

Cone-Shaped Space Target Inertia Characteristics Identification by Deep Learning With Compressed Dataset

An effective method for identifying inertia characteristics of cone-shaped space target based on deep learning is proposed. The inertia ratio is determined by the time-varying scattering fields from the cone-shaped targets. The multistatic method is introduced to reduce the evaluation time of time-varying scattering fields. The micro-Doppler spectrogram (MDS) dataset is constructed by the time–frequency analysis with numerical simulation method, point scattering model, and experimental tests. The compressed dataset is further achieved by truncated singular value decomposition (SVD). Finally, the micromotion parameter identification model is constructed to identify the inertia ratio for the cone-shaped space target. The interaction loss function and the feedforward denoising convolutional neural networks (DnCNNs) are employed to improve the identification accuracy. Parameters identification of the precession frequency, precession angle, spin frequency, and inertia ratio with both simulation and experiment datasets demonstrate the validity of the proposed method.

Micro-Doppler Parameters Extraction of Precession Cone-Shaped Targets Based on Rotating Antenna

Micro-Doppler is regarded as a unique signature of a target with micro-motions. The sophisticated recognition of the cone-shaped targets can be realized through the micro-Doppler effect. However, it is difficult to extract the micro-motion features perpendicular to the radar line of sight (LOS) effectively. In this paper, a micro-Doppler parameters extraction method of the cone-shaped targets is put forward based on the rotating antenna. First, a new radar configuration is proposed, in which an antenna rotates uniformly on a fixed circle, thus producing Doppler frequency shift. Second, the expression of the micro-Doppler frequency shift induced by the precession cone-shaped target is derived. Then, the micro-Doppler curves of point scatterers at the cone top and bottom are separated by the smoothness of the curves, and the empirical mode decomposition (EMD) method is utilized for the detection and estimation of the coning frequency. Finally, the micro-motion components perpendicular to the radar LOS are inverted by the peak of micro-Doppler frequency curve. Simulation results prove the effectiveness and robustness of the proposed method.

Parameter Estimation for Precession Cone-Shaped Targets Based on Range–Frequency–Time Radar Data Cube

A radar echo signal received from a cone-shaped target with precession contains micro-Doppler (m-D) information from different effective scattering centers. By taking full advantage of the m-D information, this paper proposes a parameter estimation algorithm for precession cone-shaped targets based on the range–frequency–time radar data cube (RDC). We build scattering center models of precession cone-shaped targets with the occlusion effect. The Binary Mask method is first utilized to obtain a high-resolution range-Doppler (RD) sequence. On this basis, the range–frequency–time RDC can be extracted from the RD sequence. In order to approach the actual case, we discuss the parameter estimation algorithm under different radar lines-of-sight (LOS). The most attractive attribute of this algorithm is that it can conduct in-depth research on m-D parameter estimation from a three-dimensional (3D) domain. Finally, the experimental results illustrate the effectiveness of the proposed method.

Precession Parameter Estimation From Wideband Measurements for 3-D ISAR Imaging of Cone-Shaped Targets

Precession of the cone-shaped warhead is the typical micromotion of ballistic targets, and 3-D inverse synthetic aperture radar (ISAR) imaging of the precession warhead is of great significance for ballistic target identification. This letter proposes a novel method for the precession parameter estimation and the 3-D ISAR imaging of the cone-shaped target. First, the imaging model of the precession cone-shaped target is developed, and the relationship between the radial-range curve on the range–slow-time plane and the precession parameters is derived. Subsequently, the wideband measurements from two-aspect radars are jointly processed to solve the target precession parameters implicitly involved in the radial-range curve of the scatterer at the cone top. Finally, based on the estimation of precession parameters, the 3-D images of the target can be reconstructed, which reveal the structure characteristics and spatial attitude of the target. Simulation results verify the effectiveness of the proposed method.

Rotational Doppler Detection of a Cone-shaped Target under the Illumination of a Vortex Electromagnetic Wave

The vortex ElectroMagnetic (EM) wave has a unique spiral phase wavefront. The rotational Doppler effect, induced by the lateral micromotion of a target is expected to provide a new mode of radar target detection. Under the illumination of vortex EM waves, the micromotion of a cone-shaped target periodically modulates the instantaneous frequency of the radar echo; this can effectively reflect the geometric characteristics and micromotion parameters of a cone-shaped target. This paper focuses on the estimation of cone-shaped target parameters under forward-looking radar conditions. First, based on the principle of vortex EM wave target rotational Doppler detection, a mathematical equation describing the vortex EM wave of a cone-shaped target echo is derived, and an echo rotational Doppler model is established. Second, a method for cone-shaped target parameter estimation under forward-looking conditions is proposed. Using the two-dimensional rotational Doppler information of the scattering points at the top and bottom of a cone-shaped target, the micromotion and geometric parameters of the target can be effectively estimated. The simulation results verify the effectiveness and robustness of the method proposed in this paper.

A Noise Robust Micro-Range Estimation Method for Precession Cone-Shaped Targets

The estimation of micro-Range (m-R) is important for micro-motion feature extraction and imaging, which provides significant supports for the classification of a precession cone-shaped target. Under low signal-to-noise ratio (SNR) circumstances, the modified Kalman filter (MKF) will obtain broken segments rather than complete m-R tracks due to missing trajectories, and the performance of the MKF is restricted by unknown noise covariance. To solve these problems, a noise-robust m-R estimation method, which combines the adaptive Kalman filter (AKF) and the random sample consensus (RANSAC) algorithm, is proposed in this paper. The AKF, where the noise covariance is not required for the estimation of the state vector, is applied to associate m-R trajectories for higher estimation accuracy and lower wrong association probability. Due to missing trajectories, several associated segments which are parts of the m-R tracks can be obtained by the AKF. Then, the RANSAC algorithm is utilized to associate the segments and the complete m-R tracks can be obtained. Compared with the MKF, the proposed method can obtain complete m-R tracks instead of several segments, and avoids the influence of unknown noise covariance under low SNR circumstances. Experimental results based on electromagnetic simulation data demonstrate that the proposed method is more precise and robust compared with traditional methods.

Laser microfabrication of conical microtargets for laser driven particle acceleration

This research is aimed at developing micrometer-level cone-shaped targets used in ultraintense laser-driven particle acceleration and improving the fabrication process and quality of results. We developed a design software that is based on a single-pass spiral-like writing of 3D structures to improve the quality of the results and lower the fabrication time. Additive manufacturing based on two-photon polymerization represents the chosen fabrication method. Targets have been investigated using electron microscopy and x-ray microtomography. In this paper, several types of cone-shaped targets have been fabricated using two-photon polymerization additive manufacturing. Structures have been fabricated with IP-L 780 photoresist, developed by Nanoscribe. Symmetric targets show best structural results, for both straight and parabolic walls, while asymmetric targets can show deformation, due to inhomogeneous voxel overlap. The spiral-like design lowers the fabrication time to a third of the time required for the fabrication of the same structures using the layer-by-layer design. The spiral-like design also shows a better overall quality of the fabricated targets. Two-photon polymerization additive manufacturing is an inexpensive and fast solution for fabricating complex 3D structures at the micrometer scale. Cone-shaped targets with parabolic walls can lower the costs and improve results for laser-driven particle acceleration. Materials are limited to appropriate transparent photoresists, when not considering complementary fabrication methods.

Instantaneous Frequency Estimation Based on Modified Kalman Filter for Cone-Shaped Target

The instantaneous frequency (IF) is a vital parameter for the analysis of non-stationary multicomponent signals, and plays an important role in space cone-shaped target recognition. For a cone-shaped target, IF estimation is not a trivial issue due to the proximity of the energy of the IF components, the intersections among different IF components, and the existence of noise. Compared with the general parameterized time-frequency (GPTF), the traditional Kalman filter can perform better when the energy of different signal components is close. Nevertheless, the traditional Kalman filter usually makes association mistakes at the intersections of IF components and is sensitive to the noise. In this paper, a novel IF estimation method based on modified Kalman filter (MKF) is proposed, in which the MKF is used to associate the intersecting IF trajectories obtained by the synchroextracting transform (SET). The core of MKF is the introduction of trajectory correction strategy in which a trajectory survival rate is defined to judge the occurrence of association mistakes. When the trajectory survival rate is below the predetermined threshold, it means that an association mistakes occurs, and then the new trajectories generated by the random sample consensus algorithm are used to correct the wrong associations timely. The trajectory correction strategy can effectively obviate the association mistakes caused by the intersections of IF components and the noise. The windowing technique is also used in the trajectory correction strategy to improve computational speed. The experimental results based on the electromagnetic computation data show that the proposed method is more robust and precise than the traditional Kalman filter. Moreover, the proposed method has great performance advantages compared with other methods (i.e., the multiridge detection, the ant colony optimization, and the GPTF methods) especially in the case of low signal noise ratio (SNR).

Bistatic Radar Coincidence Imaging Based on Multiple Measurement Vectors for Rotating Cone-Shaped Targets

Bistatic radar imaging can overcome limitations of monostatic radar imaging and obtain abundant target feature information; thus, it is followed with interest. Different from bistatic inverse synthetic aperture radar (Bi-ISAR) imaging, bistatic radar coincidence imaging (Bi-RCI) provides a new tack on the bistatic radar imaging technique. In this paper, a Bi-RCI based on multiple measurement vectors (MMV) for rotating cone-shaped targets is proposed to realize Bi-RCI coherent processing and improve imaging performance. Based on the mixed mode signals, a MMV parametric model is established and measurement number coarse selection is proposed. Finally, a modified sparse Bayesian learning (MSBL) algorithm is introduced to reconstruct the target image. Simulation results demonstrate the validity and the superiority of the proposed method.

Full-Polarization Baseband Echo Simulation of Space Targets for Bistatic Radar

Due to the high cost of obtaining echo data for space targets in real cases, most of the research usually relies on the simulation. The electromagnetic (EM) calculation is well known as one of the simulation tools. In this letter, the relationship between the calculated complex radar cross-section data and the baseband echo signal is analyzed. Then, the difference in the polarization reference plane of the radar coordinate system and EM calculation coordinate system is pointed out. For simulating the baseband echo signal with full polarization, the scattering matrix obtained by EM calculation should be rotated according to the radar position, the target position, and the target attitude. At last, a simulation of the baseband echo signal with full polarization for a cone-shaped target is given to verify the simulation algorithm.

Parametric Representation and Application of Micro-Doppler Characteristics for Cone-Shaped Space Targets

Micro-Doppler characteristics have been widely utilized to discriminate the space targets. In this paper, the micro-Doppler parametric model (mDPM) is presented to fit the micro-Doppler characteristics of cone-shaped space targets. Then, the genetic algorithm-general parameterized time-frequency transform (GA-GPTF) method is proposed to estimate the parameters of mDPM accurately. Additionally, the ratio of the two parameters of mDPM is verified to be directly related to the physical attribute of scattering centers. It can be applied to discriminate the type of scattering centers quantitatively. Finally, the electromagnetic simulation and anechoic chamber experiment verify the accuracy of mDPM and the effectiveness of the scattering center discrimination method.

Efficient Parameter Estimation for Cone-Shaped Target Based on Distributed Radar Networks

An echo signal received from a cone-shaped target with micro-motion is composed of a linear mixture of individual signals radiated from multiple effective scatterers with the occlusion effect, resulting in difficulties in parameter estimation for ballistic target discrimination (BTD). To solve this, conventional methods have been based on the sophisticated signal decomposition techniques using a 2D joint time-frequency (JTF) image or a 2D radial-range (RR) history image; however, they are inefficient for real-time BTD due to complex 2D image processing. Therefore, we propose a new parameter estimation framework consisting of five stages: 1) a normalization step; 2) signal decomposition and data association using independent component analysis in the distributed radar network; 3) estimation of dynamic parameters using 1D micro-Doppler frequency trajectories; 4) restoration of 1D RR histories; and 5) estimation of geometric parameters using the restored 1D RR histories. In particular, ICA of stage 2 is more time-saving than the conventional mathematical model-based methods using the 2D JTF image due to signal decomposition using the 1D normalized echo signals. Moreover, in the stage 4, high-quality 1D RR histories can be restored in spite of using the 2D RR history image with low resolution, compared with the conventional methods using 2D RR history image of very high resolution. In the simulations, we observed that our proposed framework is capable of performing efficient parameter estimation for the real-time BTD.

3D image reconstruction of spinning cone‐shaped target

By taking full advantage of the similarity of the two-dimensional (2D) imaging result with the target's mapping on the 2D imaging plane, this paper proposes a 3D image reconstruction method for spinning cone-shaped targets, which obtains the true 3D scattering centre distribution without image scaling and scattering centre trajectory. First, the mapping formulas for projecting the spatial point onto the imaging plane are derived. Then the 3D image reconstruction method is presented. With the proposed method, the 3D image reconstruction of spinning cone-shaped target can be implemented effectively. Numeric simulations have proved the validity of the proposed method.

Precession parameter estimation method of the cone‐shaped target based on inverse synthetic aperture radar image sequence analysis

The micro-motion features induced by space targets provide effective information for target classification and recognition. This study introduces the equivalent rotational velocity (ERV) estimation for precession parameter estimation of the cone-shaped target with inverse synthetic aperture radar image sequence. On the basis of the rotation matching relationship of precession cone-shaped target, the estimated ERV is used to build an eigenfunction, and the parameter searching set is obtained. Through theoretical analysis, the coupled parameters of the eigenfunction will concentrate on a curve on the searching plane. Thus, the parameter searching set is reduced to the neighbouring area of this curve, which reduces the computational burden greatly. Finally, the parameters are decoupled by the reconstructed radar observation angle, and the precession parameters such as precession period, precession angle, average observation angle, radial length and bottom radius of the target are precisely estimated. The proposed algorithm requires no prior information and it has high computational efficiency. Simulation results with electromagnetic data illustrate the validity of the proposed method.