Radar Target Complex High-Resolution Range Profile Modulation by External Time Coding Metasurface

Radar high-resolution range profile (HRRP) has received intensive attention from the radar automatic recognition (RATR) community. Since the initial phase of a complex HRRP is strongly sensitive to target position variation, which is referred to as the initial phase sensitivity, only the amplitude information in the complex HRRP, what is called the real HRRP, is used for RATR. This paper proposes a novel feature extraction method for the complex HRRP. The extracted complex feature vector contains the difference phase information between range cells but no initial phase information in the complex HRRP. The recognition algorithms, frame-template-database establishment methods and preprocessing methods used in the real HRRP-based RATR can also be applied to the proposed complex feature vector-based RATR. The recognition experiments based on measured data show that the proposed complex feature vector can obtain better recognition performance than the real HRRP if only the cell interval parameters are proper.

The multilayered feed-forward neural network was applied to automatic target recognition using the high range resolution (HRR) profiles in this paper. To extract effective features from the HRR profiles, the product spectrum originally proposed for the speech signal processing was introduced to the radar target recognition community. The product spectrum was defined as the product of the power spectrum and the group delay function, which could combine the information contained in the magnitude spectrum and phase spectrum of the HRR profiles and carry more details about the shape of the aircrafts. A multilayered feed-forward neural network was selected as classifier. The HRR profiles were obtained using the two-dimensional backscatters distribution data of four different scaled aircraft models. Simulations were presented to evaluate the classification performance with the product spectrum based features. The results demonstrate that the product spectrum based features outperform the original HRR profiles and the multilayered feed-forward neural network is effective for the application of automatic target recognition of aircrafts.

This study focuses on the bistatic high range resolution profiles (HRRPs) of precessing targets and considers how to utilise bistatic HRRPs for feature extraction. The movement model of the slippery bistatic scattering centres of precessing targets and the wideband bistatic echo model are introduced firstly. The dynamic simulation method of the bistatic HRRPs of the precessing target is presented using the bistatic scattering coefficients obtained via electromagnetic calculations, and the simulation results validate the theoretical analysis. Furthermore, the characteristics of the bistatic HRRP are analysed and show that the bistatic HRRP generally reflects the projection of the target onto the bisector of the bistatic angle, except that the bistatic angle is so wide that the bistatic HRRP is quite different from the monostatic HRRP. This research is helpful for imaging, and identifying precessing targets in the bistatic radar.

A high range resolution profile (HRRP) is a good characteristic for radar target, and radar target recognition by using HRRP has been studied extensively. Target recognition by fusion the target features of multiple polarimetric directions is studied in this paper, the framework of multi-polarized HRRP classification by SVM ensemble is given, and a novel approach of multi-polarized range profile classification by SVM dynamic ensemble based on fuzzy integral is proposed. Experiments on the measured multi-polarized HRRP indicate the effectiveness of our conclusion.

In recent decades, the deception jamming approach based on digital radio frequency memory (DRFM) for inverse synthetic aperture radar (ISAR) has been a well-researched topic. Various types of jamming signals based on interrupted-sampling repeater jamming (ISRJ) can induce one-dimensional multiple high-resolution range profile (HRRP) false targets or two-dimensional realistic multiple ISAR false targets for deception. However, these existing methods generate false targets that are identical to the real target. The ISRJ false target also generates a main false target whose energy is much higher than other sub-false targets. Thus, it is easy to discover that the radar has suffered jamming. In order to generate better, more confusing jamming signals, this paper proposes a jamming method based on sub-Nyquist sampling jamming to induce realistic, multiple false targets on ISAR images. It improves a template multiplication modulated time-delay method to eliminate and add scatterers on the selected false target to change its ISAR shape. The frequency-shift parameters of the template jamming signal are analyzed and derived in detail for eliminating and adding scatterers at the specified location. Thus, multiple false targets with different ISAR shapes and similar energy are generated, which can create better deception effects. Meanwhile, this method can adjust the number of false targets and the location of changed-shape false targets. Furthermore, the resolution of the false target can adaptively change with the radar pulse width and the accumulated pulse number. The simulation results show that the proposed deception jamming strategy works.

Metasurfaces exhibit remarkable abilities to manipulate electromagnetic (EM) waves and have inspired many applications in communication and radar systems. Recent advancements in metasurface research have promoted the development of various low-cost and low-complexity methods for the modulation of radar target high-resolution range profiles (HRRPs). However, existing metasurface-based methods lack the capacity to achieve simultaneous and precise control over the quantity, positions, and intensity of scattering centers in the modulated HRRP. This article addresses the challenge and proposes a radar target HRRP modulation method utilizing multifrequency time-varying metasurfaces (MFTVMs). This method allows for precise customization of the quantity, positions, and intensity of false scattering centers in HRRPs, which enables flexible modulation of HRRPs. By incorporating frequency variation into temporal modulation, the method introduces an additional degree of freedom in metasurface control, thereby enhancing the modulation capabilities of metasurfaces without compromising the effectiveness of original modulation methods. The theoretical and experimental analyses presented in this article substantiate the validity of the method. Furthermore, the concept of introducing frequency variation into the temporal control of metasurfaces may inspire further research in harmonic manipulations and radar applications.

One-dimension hierarchical local receptive fields based extreme learning machine for radar target HRRP recognition

High resolution range profile (HRRP) has the advantages of low complexity in imaging and processing. These features make HRRP widely adopted in radar automatic target recognition technology. Template matching is a representative technique for radar target recognition using HRRP. To overcome the attitude sensitivity problem of HRRP data, it is necessary to subdivide the training database according to attitude domain, and construct corresponding templates for matching. However, templates from traditional methods usually cause the loss of details due to the smoothing process, with limited classification performance. This paper is motivated by proposing a template generation approach based on maximum information profile, which retains the detailed information in templates for accuracy optimization. The proposed method is verified by the recognition experiments of three types of aircraft targets. The experimental results indicate that the maximum information profile outperform the mean range profile contributed by the angle domain template.

Polarization plays an important role in analyzing radar targets. Full polarization information has a better ability to inverse fine structures of the target. However, previous studies of high-resolution range profile (HRRP) mainly neglected effects of polarization. Therefore, a novel radar target recognition algorithm based on accelerated t-Distributed Stochastic Neighbor Embedding(t-SNE) and multi-polarization weighted fusion has been proposed. Firstly, three single polarization channels, odd and even-bounce mechanism HRRP, and full polarization HRRP are described. The accelerated t-SNE method based on Barnes-Hut approximation improves the dimensionality reduction speed of processing HRRPs while maintaining the initial information of each HRRP. Also, it is capable of visualizing different targets. With the weighted fusion of posterior probabilities of multiple support vector machines (SVMs), we are able to realize recognition of targets. Experimental results of vehicle targets based on electromagnetic calculations show that recognition performance of this algorithm is superior to other methods, and it shows strong robustness under the condition of large azimuth angle.

In this study, the support vector machine (SVM) was used as a classifier to identify aerospace objects. Radar target identification based on high resolution range profiles (HRRPs) received much attention because of its reduced complexity than those using two-dimensional (2-D) ISAR images. Therefore range profiles were used as feature vectors to represent radar data. Data sets which are for training and testing were generated by using a program called radar target backscattering simulation (RTBS) for three different target types. The performance of the SVM was compared with other classification algorithms including statistical classification techniques such as maximum likelihood (ML) and fisher linear likelihood (FLL).

In radar High Resolution Range Profile (HRRP) recognition field, an effective feature extraction process is the main point to improve whole recognition performance. As the targets' HRRP is the amplitude of coherent summations of complex return from scatters in each range cell, some data-driven compression methods, like PCA, LDA, etc. are widely used and achieve good results under experimental conditions, but still doesn't contribute much to solving the inherent problem of HRRP, such as small-sample problem. For these reasons, a series of SOM based feature extraction methods are introduced to explore the possibility in HRRP recognition. From machine learning perspective, these SOM based methods might more comprehensively describe the inherent structure relations within the original HRRP data distribution with its competitive learning process. In this paper, three category simulation experiments are presented to analysis the feature extraction, data storage, cluster result and classifier design. Of these experiments, SOM combined with multi-layer neural network can reduce the over fitting problem of directly apply the multi-layer neural network for HRRP recognition, which further prove the feasibility of SOM application in HRRP recognition.

A novel target recognition method is proposed for high‐range resolution profiles (HRRPs) of radar targets under low signal‐to‐noise ratio (SNR) conditions. This method achieves good recognition performance for noisy HRRPs with discriminative sparse‐low‐rank representation. The framework of this method is constructed based on sparse representation and low‐rank representation, which are applied to extract the local and global characteristics of target HRRPs. To guarantee the noise‐robust and highly discriminative features of the HRRPs, dictionary learning is adopted. In the training stage, a discriminative dictionary is produced based on hinge loss theory to improve the recognition performance. Denoising dictionary optimisation is implemented for noise suppression during the testing stage. Experimental results on measured HRRP data demonstrate that the proposed method can recover the original HRRPs and significantly improve the recognition performance for HRRP test samples under relatively low SNR conditions.

Stable and reliable feature extraction is crucial for radar high-resolution range profile (HRRP) target recognition. Owing to the complex structure of HRRP data, existing feature extraction methods fail to achieve satisfactory performance. This study proposes a new deep learning model named convolutional neural network–bidirectional encoder representations from transformers (CNN-BERT), using the spatio–temporal structure embedded in HRRP for target recognition. The convolutional token embedding module characterizes the local spatial structure of the target and generates the sequence features by token embedding. The BERT module captures the long-term temporal dependence among range cells within HRRP through the multi-head self-attention mechanism. Furthermore, a novel cost function that simultaneously considers the recognition and rejection ability is designed. Extensive experiments on measured HRRP data reveal the superior performance of the proposed model.

A Bayesian dynamic model based on multitask learning (MTL) is developed for radar automatic target recognition (RATR) using high-resolution range profile (HRRP). The aspect-dependent HRRP sequence is modeled using a truncated stick-breaking hidden Markov model (TSB-HMM) with time-evolving transition probabilities, in which the spatial structure across range cells is described by the hidden Markov structure and the temporal dependence between HRRP samples is described by the time evolution of the transition probabilities. This framework imposes the belief that temporally proximate HRRPs are more likely to be drawn from similar HMMs, while also allowing for possible distant repetition or “innovation”. In addition, as formulated the stick-breaking prior and MTL mechanism are employed to infer the number of hidden states in an HMM and learn the target-dependent states collectively for all targets. The form of the proposed hierarchical model allows efficient variational Bayesian (VB) inference, of interest for large-scale problems. To validate the formulation, example results are presented for an illustrative synthesized dataset and our main application-RATR, for which we consider the measured HRRP data. For the latter, we also make comparisons to the model with the independent state-transition statistics and some other existing statistical models for radar HRRP data.

A Winner- Take-All convolutional autoencoder is applied to improve the performance on the half space radar high resolution range profile(HRRP) target recognition. Feature extraction is significantly important to the radar target recognition based on the HRRP. Conventional deep models of representation learning used for HRRP target recognition commonly use the vanilla autoen-coder and deep belief net (DBN), moreover, the simulated HRRP samples used in these related work are mostly under the free space condition which is different from the real world situation. In this paper, convolution architecture autoencoder, which is more efficient in spatial feature extraction and sparse coding, is proposed. Furthermore, the half space HRRP samples, which is much more close to the real world situation and is quite different from the free space HRRP samples, is used as the dataset. Half space simulated HRRP data is used to apply the convolutional architecture on the ground target recognition and got an accuracy promotion about 7% compared to conventional vector-based module.