High Resolution Range Profile Research Articles

Variational Temporal Deep Generative Model for Radar HRRP Target Recognition

We develop a recurrent gamma belief network (rGBN) for radar automatic target recognition (RATR) based on high-resolution range profile (HRRP), which characterizes the temporal dependence across the range cells of HRRP. The proposed rGBN adopts a hierarchy of gamma distributions to build its temporal deep generative model. For scalable training and fast out-of-sample prediction, we propose the hybrid of a stochastic-gradient Markov chain Monte Carlo (MCMC) and a recurrent variational inference model to perform posterior inference. To utilize the label information to extract more discriminative latent representations, we further propose supervised rGBN to jointly model the HRRP samples and their corresponding labels. Experimental results on synthetic and measured HRRP data show that the proposed models are efficient in computation, have good classification accuracy and generalization ability, and provide highly interpretable multi-stochastic-layer latent structure.

Sparse Autoencoder Based Manifold Analyzer Model of Multi-Angle Target Feature

Automatic target recognition (ATR) has always been an important research topic, and the performance is affected by feature extraction. High-resolution range profiles (HRRP) contains structural information of target from different angles. Existing target recognition algorithms are mostly adopt the supervised learning and verify the validity through classification accuracy. However, these methods cannot satisfy the information acquisition such as correlation, composition analysis and attitude analysis of unlabeled or uncoordinated targets. Therefore, we are committed to researching new intelligent feature extraction and analysis method. In this article, a novel learning framework is proposed to realize the angle correlation feature extraction and feature analysis of multi-angle HRRP targets. A multi-layer sparse autoencoder is applied to extract HRRP features and the extracted features are mapped to the low-dimensional space by manifold learning method to find the correlation distribution of HRRP data. The effectiveness of the proposed framework is demonstrated by experiments with simulated and measured data. The experimental results show that the framework realizes the extraction of angle-invariant features, and the analysis of distribution relationship between HRRP data and angles. The research results of the correspondence between the assembly and the related geometry provide the basis and possibility for further identification and composition determination.

Recognizing the HRRP by Combining CNN and BiRNN With Attention Mechanism

In this paper, we integrate the advantages of convolutional neural network (CNN) and bidirectional recurrent neural network (BiRNN) with attention mechanism, and propose a CNN-BiRNN based method to recognize the individual high resolution range profile (HRRP). In the proposed method, the CNN is utilized to explore the spatial correlation of raw HRRP data and extract expressive features followed by a BiRNN taking the full consideration of temporal dependence between range cells. Furthermore, in order to enhance the robustness to misalignment, an attentional mechanism is employed after BiRNN to allow the CNN-BiRNN model to focus on the discriminative target area. The combination of CNN and BiRNN with attention mechanism makes the extracted features are not only efficient, but also strongly resistant to the time-shift sensitivity. Experimental results on measured HRRP data demonstrate the effectiveness and the robustness to misalignment of the proposed method.

VGM-RNN: HRRP Sequence Extrapolation and Recognition Based on a Novel Optimized RNN

High-Resolution Range Profile (HRRP) sequence has attracted academic attentions in the field of radar automatic target recognition (RATR) owing to its abundant spatial-temporal correlation between adjacent samples. However, it is difficult in the working state of radar to obtain complete HRRP sequence samples due to various internal and external factors such as ground clutter and systematic error, which poses an enormous challenge to radar target recognition. Therefore, it is crucial to repair the missing HRRPs based on the adjacent samples in the previous frames. In this paper, we discuss the extrapolate method of incomplete samples and propose an improved neural network algorithm named as Vanishing Gradient Mitigation Recurrent Neural Network (VGM-RNN). The lost samples in the sequence can be extrapolated by VGM-RNN, and the problem of vanishing gradient which is possessed in classical RNN can be effectively mitigated. The proposed method in this paper can be divided into two parts, as sample extrapolation and sequence recognition, in which sample extrapolation is the core method. Experimental results on Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that our proposed model exhibits higher accuracy and efficiency, as well as excellent anti-noise performance, compared with traditional methods. It is suggested that our proposed model can be effectively applied to radar system.

Three-Dimensional Absolute Attitude Reconstruction of a Rigid Body Based on Multi-Station HRRP Sequences

This study proposes a three-dimensional (3-D) absolute attitude reconstruction algorithm of a rigid body target based on multi-station high-resolution range profile (HRRP) sequences. The existing 3-D reconstruction algorithms rely on the target motion form and the reconstructed attitude is inaccurate due to the mismatch of the micro-motion model. In this study, the coordinates of the scattering centers are first calculated from the HRRP sequences of each radar using the factorization method. Then, the multi-station data are associated with the reconstructed scattering center coordinates, and the absolute coordinates is estimated by the relationship between the associated radar line-of-sights (LOSs) and the absolute LOSs. The 3-D reconstruction algorithms for the stable scattering centers and the sliding scattering centers are studied. Numerical simulations verify that the proposed algorithm can reconstruct the 3-D absolute attitude of a rigid body target and is not affected by the mismatch of the target micro-motion model.

Ship Target Recognition Technology of Radar High Resolution Range Profile based on Machine Learning

Ship Target Recognition Technology of Radar High Resolution Range Profile based on Machine Learning

Evaluation method for HRRP imaging quality based on intuitionistic interval fuzzy set

Aimed at the performance evaluation of radar target recognition system based on high resolution range profile (HRRP), this paper proposes an evaluation method of HRRP quality based on intuitionistic interval fuzzy set under the background that HRRP has the characteristics of translation, attitude and velocity sensitivity. Compared with the general evaluation method, this method has two improvements. One is to extend the index value from single value to interval value by interval estimation method in mathematical statistics, which introduces sample variance into calculation to improve the utilization rate of sample information. The other is to construct intuitionistic fuzzy set by using correct recognition rate derivative sequence (DSCRR) and false recognition rate derivative sequence (DSFRR), which can make full use of experimental data and improve the reliability of experimental results. The distance calculation method of intuitionistic interval fuzzy set is used to get the distance between each scheme and the ideal scheme. Finally, the score of schemes which represent different HRRP imaging quality are obtained, and the performance evaluation is completed. The evaluation results obtained from simulation validation experiments are in accordance with objective facts, which proves the validity of this evaluation method.

Analysis of TIADC channel mismatch effects on high-resolution range profile

Analysis of TIADC channel mismatch effects on high-resolution range profile

Supervised kernel discriminant local tangent space alignment for high-resolution range profile-based radar target recognition

We present a modified local tangent space alignment (LTSA) algorithm, called supervised kernel discriminant local tangent space alignment (SKDLTSA), for radar target recognition based on high-resolution range profile (HRRP). SKDLTSA aims to extract intraclass geometric structure embedded in local neighborhoods, as well as to maximize interclass separability characterized by overall distances among different classes. It is formulated with kernel technique to extract nonlinear features, which helps to obtain better performance than its linear counterparts. Extensive experiments on measured HRRP data from three flying airplanes demonstrate that the proposed method can significantly improve the recognition performance. Further results also indicate its robustness to target attitude variations and noise corruption.

Point-wise discriminative auto-encoder with application on robust radar automatic target recognition

Deep learning methods have a wide range of applications on various recognition problems for their ability to extract hierarchical features. However, most of the deep algorithms learn features from both the target area and the background area fulfilled with noise or clutter. In radar target recognition applications, the performance of applying deep learning methods directly on data with noise or clutter background is restricted because high-level features can be polluted by a significant amount of irrelevant patterns in the background. Therefore, a deep learning based model, point-wise discriminative auto-encoder, is proposed in this paper to extract noise and clutter robust features from the target area. We bind the original auto-encoder (AE) with a target area extraction net, which can learn the target area automatically from the raw data, to reduce the influence of the noise or clutter background. Moreover, we take advantage of the label information by adding a supervised constraint in our algorithm to help the target area extraction net learn the target area precisely and to extract discriminative high-level features. And then, our proposed method is applied to both high-resolution range profile (HRRP) data and synthetic aperture radar (SAR) images in radar automatic target recognition (RATR) problems. Experimental results on the measured HRRP data and SAR images show the advantages of our proposed method in real applications.

A Novel Radar HRRP Recognition Method with Accelerated T-Distributed Stochastic Neighbor Embedding and Density-Based Clustering.

High-resolution range profile (HRRP) has attracted intensive attention from radar community because it is easy to acquire and analyze. However, most of the conventional algorithms require the prior information of targets, and they cannot process a large number of samples in real time. In this paper, a novel HRRP recognition method is proposed to classify unlabeled samples automatically where the number of categories is unknown. Firstly, with the preprocessing of HRRPs, we adopt principal component analysis (PCA) for dimensionality reduction of data. Afterwards, t-distributed stochastic neighbor embedding (t-SNE) with Barnes–Hut approximation is conducted for the visualization of high-dimensional data. It proves to reduce the dimensionality, which has significantly improved the computation speed. Finally, it is exhibited that the recognition performance with density-based clustering is superior to conventional algorithms under the condition of large azimuth angle ranges and low signal-to-noise ratio (SNR).

Parametric Scattering Center Model for Canonical and Composite Dielectric Objects

The scattering center (SC) model provides a concise, explicit description of the object electromagnetic characteristics, significantly improves the efficiency of radar signal simulations, and plays an important role in model-based automatic target recognition. The existing models are mainly developed for conducting objects, and few are developed for dielectric ones. The SC models of dielectric objects are essentially different from those of their conducting counterparts due to the different scattering mechanisms. In this article, the backscattering mechanisms of canonical dielectric structures are studied, and their corresponding SC models are proposed. More importantly, the parametric SC models of the composite objects of canonical structures are first presented. These SC models are validated by comparing the radar cross section (RCS), the time–frequency representation (TFR), and the high-resolution range profile (HRRP) from the SC model with the results of the full-wave numerical method.

HRRP Clutter Rejection Via One-Class Classifier With Hausdorff Distance

Lots of detectors for high resolution range profile (HRRP) data are mainly based on the intensities of target echoes. When the intensities of outliers are as large as those of targets of interest, some false alarms will emerge during the detection stage. In this article, we propose a rejection algorithm for HRRP data between the detection stage and recognition stage to eliminate the false alarms occurring during the detection stage. In this method, the intensities and positions of the dominant scatterers are extracted as a joint feature. Then, the feature is used to develop the K-center one-class classifier based on Hausdorff distance. Experimental results on the measured HRRP data indicate that the proposed rejection algorithm can eliminate the false alarms remarkably well, and keep most of the target data as well. Meanwhile, the proposed method has better performance under different values of signal-to-noise ratio and different values of parameters than some other rejection methods.

Radar HRRP Measurement of Ships

High resolution range profile (HRRP) has become an important research direction in the field of radar target recognition. Measuring the one-dimensional HRRPs of targets and analyzing their characteristics have great significance and practical application value to target recognition. This paper has practically measured the HRRPs of ships in the sea surface utilizing the x-band phase-coherent pulse compression radar measurement platform, which lays a solid foundation for further research on ship target recognition.

Measurement matrix design for colocated MIMO radar imaging based on Compressive Sensing

Compressive Sensing (CS) is recently used in the colocated MIMO radar to reconstruct the high resolution range profiles (HRRPs) of the target. By exploiting the joint-block sparsity of the HRRPs, the reconstruction result is improved significantly. In this paper, a compressive measurement matrix is designed to reduce the amount of data. An objective for measurement matrix optimization is presented by considering the block sparsity of the HRRPs, and the Weighted Coherence Minimization algorithm is used to optimize the measurement matrix. Simulation results show that the optimized measurement matrix is superior to the Gaussians randomly measurement matrix which doesn’t utilize the block sparsity of the HRRPs.

Sparse Frequency Waveform Optimization for High-Resolution ISAR Imaging

The stepped-frequency waveform is usually used to synthesize a wideband signal in the radar imaging system. To reduce the amount of data and coherent pulse intervals (CPIs), as well as to improve antijamming abilities, the sparse stepped frequency is employed in the area of inverse synthetic aperture radar (ISAR) imaging. Nevertheless, the traditional sparse stepped linear frequency modulation waveform (SSLFMW) has a shortage of high grating lobes caused by missing frequency bands, resulting in a degradation of the ISAR imaging quality. Many methods have been proposed to reduce the effect of grating lobes by echo signal processing. However, the method of grating lobe reduction is rarely studied from the aspect of waveform optimization. In this article, a novel SSLFMW with the low grating lobes in the ISAR imaging system is proposed. By deriving the autocorrelation function (ACF), the relation between grating lobes and waveform parameters, including stepped-frequency and phase-coded elements, is established. An optimization method based on alternate iteration is designed to optimize waveform parameters and reduce grating lobes. Based on this optimized SSLFMW, we establish an ISAR imaging framework with the compressive sensing (CS) theory. Finally, the experiments are designed to show that the optimized SSLFMW has lower grating lobes. Both simulated and real measured data are used to prove that the optimized waveform has a better performance in the high-resolution range profile (HRRP) synthesis and ISAR imaging compared with the traditional SSLFMW.

Polarised HRRP scattering centre estimation via atomic norm minimisation

The combination of polarisation and high-resolution technology is a promising research direction for radar automatic target recognition. Fusing the polarisation information into the scattering centre model is able to refine the scattering structural information. This study proposes a fully-polarised radar high range resolution profile (HRRP) scattering centre estimation method based on a multiple measurement vector atomic norm minimisation model. The estimation is performed in a continuous domain, thus avoids the basis mismatch problem. The experimental results demonstrate the superiorities of the proposed method in terms of accuracy and robustness.

Polarimetric radar target recognition framework based on LSTM

Polarimetric information is of great importance for radar target recognition. Conventional polarimetric features are hand-designed based on scattering mechanism. In this study, a novel polarimetric target recognition framework based on long–short-term memory (LSTM) network is proposed. The different polarimetric channels are regarded as the sequential inputs in LSTM, and the features are extracted automatically. Experimental results on dual-polarised high-resolution range profile recognition demonstrate that the features learnt by LSTM are more discriminating than conventional features. The recognition performance of the proposed method outperforms the state-of-the-art methods as well.

Radar HRRP recognition based on CNN

In this study, ground target recognition based on one-dimensional convolutional neural network (CNN) is studied by exploiting the targets’ high-resolution range profiles (HRRPs). Contrary to conventional methods which need feature extraction artificially, CNN can automatically discover features for classification. The authors propose a multi-channel CNN architecture that can be applied on diverse forms of HRRP such as amplitude, complex, spectrum etc. Experimental results demonstrate the superiorities of the proposed method over conventional methods based on handcrafted features and single-channel CNN in terms of recognition accuracy. Visualisation of the ‘deep features’ shows higher separability than handcrafted features, thus providing an insight into its effectiveness in exploiting the intrinsic structures.

Deep forest for radar HRRP recognition

High-resolution range profile (HRRP) has received intensive attention in the radar automatic target recognition filed. Here, deep forest is applied to the recognition of HRRP. The deep forest is a deep learning method, which is a cascade of ensemble learners. In each layer, there are various ensemble learners. The input of each layer is the combination of the previous layer output and the original input data. The number of cascade levels can be adaptively determined such that the model complexity can be automatically set. Experiments based on measured data show that deep forest is feasible and achieves highly competitive performance for HRRP recognition.