High-resolution Range Profile Samples Research Articles

CVPCNN: Conditionally variational parameterized convolution neural network for HRRP target recognition with imperfect side information

Radar target recognition tasks often incorporate valuable side information (SI) that aids in the recognition task. The effectiveness of SI has been fully verified in traditional statistical modeling approaches. Its full potential remains untapped in deep learning-based high-resolution range profile (HRRP) target recognition tasks. Taking into account the azimuth sensitivity of HRRP, azimuth is chosen as the SI of the proposed model. To integrate SI into deep neural networks, we propose two novel parameterized convolution methods, namely variational parameterized convolution (VPCONV) and the extended conditionally VPCONV (CVPCONV). Specifically, VPCONV makes the convolutional kernel weights as the function of the auxiliary azimuth, which gives the network the capability of adaptively adjusting to changes in azimuth. Acknowledging the challenge of estimating HRRP azimuth accurately in practical scenarios, VPCONV employs a heteroscedastic Gaussian distribution, featuring varying mean and variance, to generalize the estimation error of SI through variational encoding. Moreover, CVPCONV incorporates the properties of the HRRP samples themselves by kernel attention module. Finally, we present a lightweight SI-based network. The experimental results based on the measured HRRPs validate the effectiveness of proposed method across various extended recognition tasks, underscoring the potential of fusing SI via parameterized convolution in advancing target recognition systems.

Radar HRRP Target Recognition Based on Dynamic Learning with Limited Training Data

For high-resolution range profile (HRRP)-based radar automatic target recognition (RATR), adequate training data are required to characterize a target signature effectively and get good recognition performance. However, collecting enough training data involving HRRP samples from each target orientation is hard. To tackle the HRRP-based RATR task with limited training data, a novel dynamic learning strategy is proposed based on the single-hidden layer feedforward network (SLFN) with an assistant classifier. In the offline training phase, the training data are used for pretraining the SLFN using a reduced kernel extreme learning machine (RKELM). In the online classification phase, the collected test data are first labeled by fusing the recognition results of the current SLFN and assistant classifier. Then the test samples with reliable pseudolabels are used as additional training data to update the parameters of SLFN with the online sequential RKELM (OS-RKELM). Moreover, to improve the accuracy of label estimation for test data, a novel semi-supervised learning method named constraint propagation-based label propagation (CPLP) was developed as an assistant classifier. The proposed method dynamically accumulates knowledge from training and test data through online learning, thereby reinforcing performance of the RATR system with limited training data. Experiments conducted on the simulated HRRP data from 10 civilian vehicles and real HRRP data from three military vehicles demonstrated the effectiveness of the proposed method when the training data are limited.

Tensor RNN With Bayesian Nonparametric Mixture for Radar HRRP Modeling and Target Recognition

To deal with the temporal dependence between range cells in high resolution range profile (HRRP), dynamic methods, especially recurrent neural network (RNN), have been employed to extract features for target recognition. However, RNN has difficulty in complex and diverse sequence modeling problems as it ignores non-stationary sequential relationship between time-steps by sharing same parameters among all time-steps. Given this issue, we propose tensor recurrent neural network with Gaussian mixture model (GmTRNN) for HRRP, not only making use of temporal characteristic but also modeling the variation among its patterns. Specifically, a novel tensor RNN is developed by extending all the parameters in the form of tensor to explore diverse temporal dependence between range cells within an HRRP sample, where a mixture model is introduced to determine the parameters of each time-step in tensor RNN. Moreover, to take advantage of Bayesian nonparametrics in handling the unknown number of mixture components, we further propose the tensor recurrent neural network with Dirichlet process mixture (DPmTRNN). For scalable and joint training of clustering and recognition, we present effective hybrid online variational inference and stochastic gradient descent method. Experiments on benchmark data, measured and simulated HRRP data demonstrate the the effectiveness and efficiency of our models and its robustness to HRRP shift.

Class factorized complex variational auto-encoder for HRR radar target recognition

In the field of radar automatic target recognition (RATR), the high-resolution range profile (HRRP) has received intensive attention. Bar a few exceptions, almost all HRRP-based ATR classification systems ignore the phase of the HRRP when the data is input to the classifier, relying instead only on the magnitude of the complex HRRP samples. This approach ignores the phase of the complex HRRPs, which reduces the information in the signal. In this paper, we develop a novel class factorized complex variational auto-encoder (CFCVAE) to utilize the phase of the high range resolution (HRR) radar echo for recognition. The CFCVAE is a complex-valued neural network (CV-NN) consisting of the encoding and decoding modules. In CFCVAE, the encoding module projects the observed data into the latent space, and then the latent features are fed to the decoding module, which further maps the latent features to data. In particular, the decoding module introduces the class labels to partition the whole observations into some parts, each of which is depicted by a specific class-decoder. Compared with the traditional variational auto-encoder (VAE) containing a single decoder, the CFCVAE can give a more accurate description to the whole dataset via multiple class-decoders, thus improving the characterization ability of features. In addition, based on the class labels, the CFCVAE employs the conditional prior on the latent variable to enhance the discrimination of features. Moreover, a complex backpropagation algorithm is derived for CFCVAE training, and a sample is classified to the class corresponding to the class-decoder with the minimum reconstruction error in the test stage. Experimental evaluations on the measured data indicate that the proposed method indeed achieves very promising target recognition performance.

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.

Statistical Modeling With Label Constraint for Radar Target Recognition

Motivated by the problem of radar target recognition, we develop a label-aided factor analysis (LA-FA) model for statistical modeling of high-resolution range profile (HRRP) under the prerequisite that the HRRP data are Gaussian distributed. The LA-FA model is the extension of the multitask learning-based factor analysis (MTL-FA) model, which is mainly applied to the recognition problem with small training data size. Compared to the MTL-FA model, our LA-FA model introduces the discrete class labels via Sigmoid-Bernoulli hierarchy to restrict the learning of model parameters, which offers the potential to enhance the separability of statistical models from different classes, thus beneficial to the improvement of recognition capability. In addition, since the noise level of a test sample is usually different from those of the training samples in the real application, we introduce a noise-robust modification method for Gaussian-based models. The proposed modification method is implemented by updating the noise level parameter of the statistical model according to the estimated signal-to-noise ratio (SNR) of test HRRP. Experiments on measured HRRP data demonstrate the better recognition performance of the LA-FA model with limited training data and our noise-robust model modification method under low test SNR. Especially, when there are 20 training HRRP samples per frame, the recognition rate of our LA-FA model is 7% higher than that of the MTL-FA model, and moreover, the recognition accuracy of the noise-robust LA-FA model is 3% higher than that of the LA-FA model without modification under the condition of SNR = 15 dB.

Factorized discriminative conditional variational auto-encoder for radar HRRP target recognition

Abstract In this paper, we propose a conditional generative model for radar high resolution range profile (HRRP) target recognition to learn the discriminative representations and sufficiently encode the observed feature variability by taking the multi-layer perception (MLP) as the sufficient statistics of posterior approximation distribution, thus offering the potential to improve the overall recognition performance. Considering the target-aspect sensitivity of HRRP, the model is regularized through reconstructing the average profiles. Then we introduce three-way weight tensors for MLPs to capture the multiplicative interactions between label information and HRRP samples, which are then further factorized to effectively reduce model parameters. The extensive experimental results on the measured HRRP data demonstrate that the proposed algorithm achieves the promising target recognition and reconstruction performance.

Target-Aware Recurrent Attentional Network for Radar HRRP Target Recognition

Abstract In this paper, we develop a Target-Aware Recurrent Attentional Network (TARAN) for Radar Automatic Target Recognition (RATR) based on High-Resolution Range Profile (HRRP) to make use of the temporal dependence and find the informative areas in HRRP, since it reflects the distribution of scatterers in target along the range dimension. Specifically, we utilize RNN to explore the sequential relationship between the range cells within a HRRP sample and employ the attention mechanism to weight up each timestep in the hidden state so as to discover the target area, which is more discriminative and informative. Effectiveness and efficiency are evaluated on the measured data. Compared with traditional methods, besides the competitive recognition performance, TARAN is also more robust to time-shift sensitivity thanks to the memory function of RNN and attention mechanism. Furthermore, detailed analysis of TARAN model are provided based on time domain and spectrogram features.

Similarity preserving multi-task learning for radar target recognition

In this study, a statistical recognition model based on similarity preserving multi-task learning (SP-MTL) is developed for radar target recognition of high-resolution range profile (HRRP) data. A similarity preserving constraint, which describes the similarity information of HRRP samples, is introduced into multi-task learning to enhance the discriminative capability of the statistical model with limited training data. In addition, the SP-MTL model can be applied to the model prediction of new data based on transfer learning theory. Experiments on measured data show that the proposed model can achieve better recognition performance than traditional methods when training data is small. The application of the SP-MTL model to model prediction based on transfer learning theory can improve the learning precision of the new statistical model compared with the single-task learning of new data.

Radar HRRP Target Recognition Based on t-SNE Segmentation and Discriminant Deep Belief Network

In radar high-resolution range profile (HRRP)-based target recognition, one of the most challenging tasks is the noncooperative target recognition with imbalanced training data set. This letter presents a novel recognition framework to deal with this problem. The framework is composed of two steps: first, the t-distributed stochastic neighbor embedding (t-SNE) and synthetic sampling are utilized for data preprocessing to provide a well segmented and balanced HRRP data set; second, a discriminant deep belief network (DDBN) is proposed to recognize HRRP data. Compared with the conventional recognition models, the proposed framework not only makes better use of data set inherent structure among HRRP samples for segmentation, but also utilizes high-level features for recognition. Moreover, the DDBN shares latent information of HRRP data globally, which can enhance the ability of modeling the aspect sectors with few HRRP data. The experiments illustrate the meaning of the t-SNE, and validate the effectiveness of the proposed recognition framework with imbalanced HRRP data.

Radar HRRP recognition based on discriminant deep autoencoders with small training data size

A novel radar high resolution range profile (HRRP) recognition method based on discriminant deep autoencoders is proposed to enhance the classification performance with limited training samples. Compared with the conventional models, the proposed method not only extracts high-level feature which can reflect physical structure of HRRP, but also trains HRRP samples globally to reduce the requirement of the training data. The experiment based on the measured data demonstrates the physical meanings of the extracted feature. Moreover, the recognition performance of the proposed method consistently outperforms the conventional models, and the improvement become more significant with smaller training data size.

Noise-Robust Modification Method for Gaussian-Based Models With Application to Radar HRRP Recognition

In this letter, we introduce a novel noise-robust modification method for Gaussian-based models to enhance the performance of radar high-resolution range profile (HRRP) recognition under the test condition of low signal-to-noise ratio (SNR), and we develop an efficient scheme for its computation. This noise-robust modification method is implemented by revising the trained Gaussian-based model according to the estimated SNR of test HRRP. We apply the proposed method to adaptive Gaussian classifier and truncated stick-breaking hidden Markov model. Experimental results demonstrate that the proposed method can significantly improve the average recognition rate for noisy HRRP test samples while offering recognition performance comparable to that of original model for clean HRRP test samples. Moreover, even when the SNR of test HRRP samples is not precisely estimated, we can still obtain an acceptable result with the proposed method.

NONLINEAR SUBPROFILE SPACE FOR RADAR HRRP RECOGNITION

In this paper, a novel approach, namely nonlinear subprofile space (NSS), is proposed for radar target recognition using high-resolution range profile (HRRP). First, the HRRP samples are mapped into a high-dimensional feature space using nonlinear mapping. Second, the nonlinear features, namely nonlinear subprofiles, are extracted by nonlinear discriminant analysis. Then, for each class, the nonlinear subprofile space is formed using all the training nonlinear subprofiles of class. Finally, the minimum hyperplane distance classifier (MHDC) is used for classification. The aim of NSS method is to represent the feature area of target using nonlinear subprofile space, and effectively measure the distance between the test HRRP and feature area via minimum hyperplane distance (MHD) metric. The experimental results of measured data show that the proposed method has better performance of recognition than KPCA and KFDA.

Bayesian Spatiotemporal Multitask Learning for Radar HRRP Target Recognition

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.

Radar HRRP Statistical Recognition: Parametric Model and Model Selection

Statistical modeling for radar high-resolution range profile (HRRP) is a challenging task in radar HRRP statistical recognition. Theoretical analysis and experimental results show that elements in an HRRP sample are statistically correlated and non-Gaussian distributed. First, this paper introduces three joint-Gaussian models, i.e., subspace approximation model, probability principal components analysis (PPCA) model and factor analysis (FA) model, into radar HRRP statistical recognition. Due to the experimental results, we can have the conclusion that the jointly non-Gaussian distributed HRRP samples approximately follow the joint-Gaussian distribution described by FA model. Therefore, we can apply FA model to radar HRRP statistical recognition rather than a joint-Gaussian mixture model, e.g., PPCA mixture model or FA mixture model, which is a more accurate choice for modeling non-Gaussian distributed correlations in multidimensional data but with high learning complexity and large computation burden, and the difficulty in the statistical modeling for HRRP samples is largely reduced. Second, this paper concerns model selection of FA model in radar HRRP statistical recognition, in which there are two issues, i.e., the partition of target-aspect frames and the determination of the number of factors in each frame. Based on the Akaike information criterion (AIC) and the Bayes' information criterion (BIC), an iterated algorithm for model selection is proposed in this paper, which can automatically give the optimal aspect-frame boundaries and determine the optimal number of factors in each aspect-frame. The recognition experiments based on measured data show that the proposed adaptive partition approach can further improve the recognition performance with higher recognition efficiency.

Radar HRRP statistical recognition based on hypersphere model

The theoretical analysis and experimental results in this paper show that the independence assumption regarding elements in a radar high-resolution range profile (HRRP) sample, under which some statistical recognition methods were proposed, is not true. In addition, geometrically speaking, target HRRP samples spread on a unit hypersphere, since L 2-normalized HRRP samples are applied to radar automatic target recognition (RATR) to deal with the amplitude-scale sensitivity problem. Therefore, this paper considers the two issues and proposes a novel statistical recognition method based on hypersphere model for power transformed HRRP samples under the jointly multivariate Gaussian distribution hypothesis. Compared with the conventional principal components analysis (PCA)-based subspace statistical recognition method, the hyperspherical spread of HRRP samples and the effectively discriminating information contained in the noise subspace can be fully utilized in this method without increasing computation complexity. The experimental results based on measured data show that our proposed method can greatly improve the recognition performance.