Lan Du

Radar HRRP target recognition based on higher order spectra

Radar high-resolution range profile (HRRP) is very sensitive to time-shift and target-aspect variation; therefore, HRRP-based radar automatic target recognition (RATR) requires efficient time-shift invariant features and robust feature templates. Although higher order spectra are a set of well-known time-shift invariant features, direct use of them (except for power spectrum) is impractical due to their complexity. A method for calculating the Euclidean distance in higher order spectra feature space is proposed in this paper, which avoids calculating the higher order spectra, effectively reducing the computation complexity and storage requirement. Moreover, according to the widely used scattering center model, theoretical analysis and experimental results in this paper show that the feature vector extracted from the average profile in a small target-aspect sector has better generalization performance than the average feature vector in the same sector when both of them are used as feature templates in HRRP-based RATR. The proposed Euclidean distance calculation method and average profile-based template database are applied to two classification algorithms [the template matching method (TMM) and the radial basis function network (RBFN)] to evaluate the recognition performances of higher order spectra features. Experimental results for measured data show that the power spectrum has the best recognition performance among higher order spectra.

A two-distribution compounded statistical model for Radar HRRP target recognition

In the statistical target recognition based on radar high-resolution range profile (HRRP), two challenging tasks are how to deal with the target-aspect, time-shift, and amplitude-scale sensitivity of HRRP and how to accurately describe HRRPs statistical characteristics. In this paper, based on the scattering center model, range cells are classified, in accordance with the number of predominant scatterers in each cell, into three statistical types. After resolving the three sensitivity problems, this paper develops a statistical model comprising two distribution forms, i.e., Gamma distribution and Gaussian mixture distribution, to model echoes of different types of range cells as the corresponding distribution forms. Determination of the type of a range cell is achieved by using the rival penalized competitive learning (RPCL) algorithm, while estimation for the parameters of Gamma distribution and Gaussian mixture distribution by the maximum likelihood (ML) method and the expectation-maximization (EM) algorithm, respectively. Experimental results for measured data show that the proposed statistical model not only has better recognition performance but also is more robust to noises than the two existing statistical models, i.e., Gaussian model and Gamma model.

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.

Noise Robust Radar HRRP Target Recognition Based on Multitask Factor Analysis With Small Training Data Size

A factor analysis model based on multitask learning (MTL) is developed to characterize the FFT-magnitude feature of complex high-resolution range profile (HRRP), motivated by the problem of radar automatic target recognition (RATR). The MTL mechanism makes it possible to appropriately share the information among samples from different target-aspects and learn the aspect-dependent parameters collectively, thus offering the potential to improve the overall recognition performance with small training data size. In addition, since the noise level of a test sample is usually different from those of the training samples in the real application, another contribution is that the proposed framework can update the noise level parameter in the FA model to adaptively match that of the received test sample. Efficient inference is performed via variational Bayesian (VB) for the proposed hierarchical Bayesian model, and encouraging results are reported on the measured HRRP dataset with small training data size and under the test condition of low signal-to-noise ratio (SNR).

Hierarchical Classification of Moving Vehicles Based on Empirical Mode Decomposition of Micro-Doppler Signatures

A novel method is proposed for moving wheeled vehicle and tracked vehicle classification using micro-Doppler features from returned radar signals within short dwell time. In this method, an adaptive analysis technique called Empirical Mode Decomposition (EMD) is utilized to decompose the motion components of moving vehicles, and a hierarchical classification structure using the decomposition results of returned signals is proposed to discriminate the two kinds of vehicles. The first stage of the structure elementarily identifies the tracked vehicle data by checking the existence of its unique feature and a further classification via our proposed features based on EMD is implemented in the second stage by using Support Vector Machine (SVM) classifier. Experimental results based on the simulated data and measured data are presented, including the performance analysis for low signal-to-noise ratio (SNR) case, generalization evaluation for different target circumstances and comparison with some related methods.

Robust Classification Scheme for Airplane Targets With Low Resolution Radar Based on EMD-CLEAN Feature Extraction Method

A novel classification scheme is proposed to categorize airplane targets into three kinds, i.e., turbojet aircraft, prop aircraft, and helicopter based on the jet engine modulation (JEM) characteristics of their low resolution radar echoes. From the pattern classification viewpoint, the low-dimensional feature vector is extracted via a two-step feature extraction algorithm based on empirical mode decomposition method and CLEAN technique. The feature extraction method can separate the fuselage component and JEM component in the target echo, and sufficiently utilize the information within or between the two components to extract the discriminative features for the three kinds of aircrafts. In addition, because the noise level of a test sample is usually different from those of the training samples in the real application, a simple and efficient preprocessing method is proposed for the classification stage to denoise the received test sample. Experimental results based on the simulated and measured data are presented, including the performance analysis for different dwell time, pulse repeat frequency (PRF) or signal-noise-ratio (SNR) cases and comparison with some related methods.

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.

Target classification with low-resolution radar based on dispersion situations of eigenvalue spectra

Most low-resolution radar systems, especially ground surveillance radar systems, work at relatively low pulse repeat frequency (PRF) and with short time-on-target (TOT) (duration in scanning). Low PRF leads to Doppler ambiguity and short TOT results in low Doppler resolution, which poses a problem to target classification with low-resolution radar based on the jet engine modulation (JEM) characteristic of radar echo. From the pattern classification viewpoint, we propose a method of using dispersion situations of JEM eigenvalue spectra to categorize aeroplanes into three kinds, namely turbojet aircraft, prop aircraft and helicopter. We analyze the mathematical model of JEM echoes consisting of a series of line spectra and regard them as a sum of several series of harmonious waves. Classification features can be extracted based on the harmonious wave sum model. Some schemes for extracting features from echoes within or between pulses are proposed. Low-dimensional features are extracted to reduce computation burden. Our methods do not compensate for the fuselage echoes and are insensitive to the variation of fuselage Doppler. The feasibility of our methods is demonstrated by simulation experiment.

Sticky Hidden Markov Modeling of Comparative Genomic Hybridization

We develop a sticky hidden Markov model (HMM) with a Dirichlet distribution (DD) prior, motivated by the problem of analyzing comparative genomic hybridization (CGH) data. As formulated the sticky DD-HMM prior is employed to infer the number of states in an HMM, while also imposing state persistence. The form of the proposed hierarchical model allows efficient variational Bayesian (VB) inference, of interest for large-scale CGH problems. We compare alternative formulations of the sticky HMM, while also examining the relative efficacy of VB and Markov chain Monte Carlo (MCMC) inference. To validate the formulation, example results are presented for an illustrative synthesized data set and our main application-CGH, for which we consider data for breast cancer. For the latter, we also make comparisons and partially validate the CGH analysis through factor analysis of associated (but distinct) gene-expression data.

Noise Robust Radar HRRP Target Recognition Based on Scatterer Matching Algorithm

Since the signal-to-noise ratio (SNR) directly relates to the distance between the target and the radar for a given noise power and radar power, the noise robustness of a recognition algorithm is very important to increase the recognition distance between the target and the radar in the real application. In this paper, a novel noise-robust recognition method for high-resolution range profile (HRRP) data is proposed to enhance its recognition performance under the test condition of low SNR. The target dominant scatterers are first extracted based on the scattering center model of complex HRRP data via the orthogonal matching pursuit algorithm. Then, a scatterer matching recognition algorithm based on Hausdorff distance is developed with the magnitudes and locations of extracted dominant scatterers used as the feature patterns. Here, the noise reduction is accomplished based on the sparse distribution property of dominant scattering centers in a target. Experimental results on the synthetic and measured HRRP data demonstrate that the proposed method can improve the recognition performance under the relatively low SNR condition for both orthogonal and superresolution representations of scattering center model.