Radar target classification based on support vector machines and High Resolution Range Profiles

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.

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.

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.

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.

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.

This paper is focused on the feature extraction techniques of radar high range resolution profiles (HRRPs). In order to release the translational sensitivity of HRRPs, two translation invariant features, the central moments and distribution entropy, are extracted from the HRRPs and combined to form a new feature vector. Experiment on real data of three airplanes in flight is implemented to evaluate the recognition performance of the combined feature, using the nearest neighbour (NN) classifier and the support vector machine (SVM) classifier, respectively. Experimental results demonstrate that the combined feature can significantly enhance the separability of different targets and improve the average recognition rate of HRRP target recognition.

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

In this paper, we propose the aircrafts supervised classification using the high range resolution profiles (HRR Profiles), extracted from ISAR images. The principal components are extracted from pre-processed synthetic radar data before the 1 nearest neighbour- Inn classification . The classification performances are presented in terms of the correct classification probability (Pec). (4 pages)

Aiming at the Carrier-Free Ultra-Wide-Band Radar (CF-UWBR) under the near-range conditions, a novel target recognition method based on the Gram Angle Field (GAF) feature extraction and the Inception V3 network is proposed in this paper. The one-dimensional High-Resolution Range Profile (HRRP) of CF-UWBR is highly sensitive to observation angle so that the performance of direct recognition using the original time-domain HRRP data is not satisfying. In response to this problem, the GAF method is employed to convert the HRRP data into a two-dimensional image firstly, to maintain and highlight the correlation features between the adjacent time points in the polar coordinate domain. Furthermore, the Inception V3 network is selected for the target recognition based on the dual-mode GAF features, due to the excellent performance with the fewer computing resources. The experimental results on the actual measured CF-UWBR data set indicated that the proposed method is superior in performance to the methods based on the Support Vector Machine (SVM), the Gated Recurrent Unit (GRU) network, or the Convolutional Neural Network (CNN) with the original HRRP image. Especially when the dual-mode GAFs are used, the recognition accuracy of 97.63% can be achieved, about 1.95% higher than the STOA.

Radar automatic target recognition (RATR) plays a significant role in military applications. The high resolution range profiles (HRRP) contain a large amount of information, such as the distribution of the scatterers, target size and structure, so the HRRP has great prospects in the field of RATR. The traditional HRRP based RATR systems need to extract features manually, which is very difficult and complex to obtain excellent features. To solve this problem, this paper proposed a bidirectional simple recurrent unit network based on soft attention mechanism (SAMBi-SRU) to achieve HRRP target recognition. The simulation results demonstrate that the proposed model can extract robust features from the HRRP data effectively and obtain good performance and noise immunity in HRRP based target recognition.

Radar automatic target recognition (RATR) based on high resolution range profiles (HRRPs) has received intensive attention in recent years. The data-driven HRRP recognition methods based on neural networks have achieved outstanding performance with a complete target-aspect template library. However, such methods can barely achieve satisfactory performance under the condition of incomplete target-aspects or low signal-to-noise ratio. In order to solve this problem, a prior-knowledge guided neural network (PriorK-NN) is proposed for HRRP target recognition, which incorporates prior-knowledge about scattering centers and target-aspects into neural networks. Firstly, by combining the physical generative mechanism of HRRP with neural networks, we proposed an interpretable scattering center layer (SC-layer). The SC-layer can effectively reduce the adverse effects of noise and extract the location and intensity of target dominant scattering centers. In addition, we proposed a loss function named targetaspect supervised contrastive loss based on the prior-knowledge about target-aspects. By using the proposed loss function, clusters of HRRPs belonging to the same class but with different target-aspects are pulled together, while simultaneously pushing apart clusters of HRRPs from different classes. Therefore, the proposed loss function can improve the recognition performance under the condition of incomplete target-aspects. Experiments on the aircraft electromagnetic simulation dataset and the measured dataset validated the effectiveness of our proposed method on noise-corrupted HRRP recognition and demonstrated superior performance compared with other HRRP recognition methods under the condition of incomplete target-aspects.

The radar high resolution range profiles has been widely used in radar imaging and target recognition because of the simplicity and effectiveness to obtain. For the GTD model of radar target, this paper studies the reconstruction and feature extraction of high resolution range profiles using the method of sparse decomposition. It can avoid the drawbacks of the Nyquist sampling theorem and process the radar echo signal with the method of closer to the essence of the signal characteristics. The experimental results show the effectiveness of the signal reconstruction and feature extraction.

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.

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.

In this article, we propose a method based on time coding metasurface (TCM) to modulate the characteristics of radar targets in complex high-resolution range profiles (HRRPs). Unlike the existing works where metasurfaces are typically attached to target surfaces, in our work, the metasurface and controlled target are separable, making modulation more flexible. The proposed method involves controlling the equivalent scattering phase of distance offset false targets within the HRRP, which are generated through TCM by adjusting the modulation parameters. The scattering characteristics of the false target and the controlled target are combined within the same distance unit to create a superimposed scattering feature vector. By taking into account the influence of interference effect, we are able to modulate the scattering characteristics of radar targets within the complex HRRP. The proposed method was validated through theoretical analysis, simulation experiments, and actual experiments conducted in various scenarios. The experimental results demonstrate that the false targets generated by the external TCM can be used to enhance or weaken the scattering intensity of the original target in HRRP, thereby changing the overall image characteristics and posing challenges to target recognition. This method can achieve the modulation of radar target image characteristics under noncoverage conditions, expanding the application scenarios of metasurfaces in the field of radar electronic countermeasures (ECM).