Automatic radar target recognition using superresolution music 2D images and self-organizing neural network

This paper considers the use of the software package WinBUGS for an application to a previously documented technique for automatic radar target recognition. Mixture models under a Bayesian formalism are used to model the probability densities of radar measurements from given classes. WinBUGS is then used to construct a Markov chain Monte Carlo sampler that allows implementation of this Bayesian approach. The model developed is applied to radar range profiles from seven classes of military ships.

This paper describes the US Army's efforts under the ATR relational template matching program to develop a multi-sensor forward-looking IR (FLIR) and millimter wave (MMW) radar automatic target recognition (ATR) algorithm. The general problem consists of identifying ground targets at low depression angles of less than 6 degrees and ranges of 500 to 6000 m. However, the algorithm is directly applicable to other target sets, including air targets, and ground targets at high depression angles. Our phase I goal was to provide a proof- of-principle demonstration of this new model-based methodology. We did this on simulated FLIR imagery for a three target class problem. A comprehensive test and evaluation was performed at the Night Vision and Electronic Sensors Directorate. Our goal in phase II was to demonstrate this new algorithm approach on realistic field-collected second-generation FLIR imagery with a larger target set consisting of ten ground vehicles. With phase III, our goal was to demonstrate multisensor fusion on FLIR (first generation) fused with laser radar. This was demonstrated on a four-ground-target-class problem. The limitations to first-generation FLIR data and four targets was due purely to restrictions of the field-collected data. Our goal in phase IV is to demonstrate this methodology for FLIR fused with MMW radar data. Phase IV will be a three-year effort, with the first year concentrating on using the MMW radar data for detection, reduction in false alarms, and the provision of accurate range (i.e., scale) to the FLIR algorithm. The goal of the second and third years will be to fuse the MMW data into not only the detection phase of the algorithm but also the recognition and identification portions of the algorithm. This paper presents the general model-based methodology used for the FLIR and the neural network methodology used to provide the FLIR with the MMW detections and scale. Results are provided for the FLIR-only algorithm for short range and moderate range scenarios given an accurate estimate of target range.

This paper presents a part of research study dealing with the initialization of an information system on radar automatic target recognition (ATR) chain. In this framework, we propose a set of helpful information at different levels of the ATR chain for decision making. The methodology used is the knowledge discovery process from data (KDD process).we focused in this paper on the data preparation step, more precisely Speckle denoising, edge detection and computation of features vector. We present the results of a several speckle filters that are largely used by ISAR imaging scientists (Lee, Frost, Kuan...). Fourier Descriptors have been used as features of the objects. Various similarity measures have been used and compared for target recognition.

Radar target automatic recognition is a comprehension problem. We have researched the practical radar data and concluded some valuable viewpoint. With it we put forward a whole scheme to achieve radar automatic target recognition. First the OS CAFR and binary accumulation algorithm is taken to accurately detect target. Here we lower self-adapt threshold to get more targets which maybe be false target. Then we use target trace management to remove the illusion target. The novel algorithm, called as endpoint detection based on moving window, can get more valuable data to classification. For 3D target we must build more templets in classification. So we use Neural Net as classification algorithm to reduce memory space. The simulation results prove that this scheme can get better recognition results.

This study extends the theory of the micro-Doppler effect into the multistatic domain, and considers how the multistatic micro-Doppler signature (µ-DS) will affect radar automatic target recognition (ATR). Real multistatic µ-DS of personnel targets are examined and their nature compared with theory and simulated results. It is demonstrated that the use of multistatic µ-DSs would increase the robustness of radar ATR systems to the problem of self-occlusion, where the target obscures itself. Redundancy in the multistatic µ-DS is identified, and how this may reduce the data fusion demands of multi-aspect classifiers is discussed. Also, information theory is used to demonstrate that the multistatic µ-DS contains more target information than the monostatic case. This result leads to the prediction that multi-aspect µ-DS-based radar ATR would have improved performance compared to single-aspect solutions.

In order to effectively solve the problems such as fault with single features, carrying the lack of information resulting in poor effect of fault judgment in traditional transformer fault diagnosis method. In view of this, this paper will consider the insulation resistance of the core 12 fault symptoms and fault characteristic gases as the combination of fault characteristics, effectively solve the problem of the existence of the feature quantity; At the same time, the principal component analysis and self-organizing neural network combination is introduced to this paper, this paper puts forward a new method of transformer fault diagnosis based on principal component analysis and self-organizing neural network. Firstly, using the principal component analysis for the main components of 17 kinds of feature extraction, extracted features can effectively present the characteristics of 10 kinds of fault classification, on this basis, the 10 methods of feature extraction was obtained as input vector of self-organizing neural network and identification of fault types. The example analysis shows that this method has a good diagnostic effect, which can effectively improve the accuracy of fault diagnosis and verify the validity and feasibility of this method.

In the radar automatic target recognition (RATR) system using high resolution range profile (HRRP), if a test sample has not been trained in training phase, it would lead to a full miss classification in test phase. In this paper, we design a classifier based on generalized confidence, which can efficiently refuse-recognize a new target. Firstly, principal component analysis (PCA) method is used to extract feature vector from every sample. Secondly, the classifier calculates each feature vector's generalized confidence on mahalanobis distance. Consequently, the distribution of confidence is used to define a refuse-recognition threshold for each training target. In test phase, for each trained-target in the database, we suppose that the test sample belongs to current target, calculate the generalized confidence, judge whether the test sample really belongs to the target or not via comparing the confidence with the target's refuse-recognition threshold. The final class is determined by vote. The experimental results demonstrate the effectiveness of the proposed algorithms.

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

In this letter, we present a novel generic approach for radar automatic target recognition in either inverse synthetic aperture radar (ISAR) or synthetic aperture radar (SAR) images. For this purpose, the radar image is described by a statistical modeling in the complex wavelet domain. Thus, the radar image is transformed into a complex wavelet domain using the dual-tree complex wavelet transform. Afterward, the magnitudes of the complex sub-bands are modeled by Weibull or Gamma distributions. The estimated parameters of these models are stacked together to create a statistical dictionary in training step. For the recognition task, we use the weighted sparse representation-based classification method that captures the linearity and locality information of image features. In this context, we propose to use the Kullback–Leibler divergence between the parametric statistical models of training and test sets in order to assign a weight for each training sample. Experiments conducted on both ISAR and SAR images’ databases demonstrate that the proposed approach leads to an improvement in the recognition rate.

Three-dimensional (3-D) imaging and the 3-D micromotion feature extraction technique of space targets provide significant information for target classification and recognition. The idea of multiantenna interferometric processing from the interferometric inverse synthetic aperture radar (InISAR) is introduced into the 3-D imaging of micromotional space targets. After the modeling analysis of the micromotion characteristic induced by the target on the range–slow-time plane, a 3-D interferometric imaging method for space precession targets in wideband radar is proposed. The real 3-D images of the target in both the normal mode and the squint mode are obtained by the proposed method. Meanwhile, some 3-D precession features and structure parameters of the target are extracted accurately. Finally, simulation results validate the effectiveness and robustness of the proposed method. Compared with the existed approaches, this study can achieve real 3-D imaging and extract precession features and structure parameters for space precession target only by a monostatic radar with multi-antenna.

A hybrid approach to automatic target recognition (ATR) combining the complementary strengths of conventional image processing algorithms, artificial neural networks, and knowledge based expert systems is presented. The architecture employs parallel feature and pixel processing channels, the former using a self-organizing neural network and the latter using a supervised learning neural network. The feasibility of the hybrid automatic target recognition (ATR) approach to target detection, classification and recognition is demonstrated using (LADAR) data. >

In the radar automatic target recognition (RATR) community, high-resolution range profile (HRRP) obtained from the target has been widely utilized for many recognition techniques. The information provided by one single HRRP is limited and unstable, utilizing multiple HRRPs for recognition is a better choice. However, common method such as the hidden Markov model (HMM) is not good at modeling high dimensional data and can only capture local dependencies. In this paper, a multi-scale temporal model is developed from the conditional restricted Boltzmann machine (CRBM). The proposed model can efficiently discover stochastic patterns in high dimensional data just as RBMs generally do. Besides that, it can capture multiple scales of temporal dependencies, which is especially beneficial when the angular sampling rate at testing phase is different from that at training phase. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database validate the efficiency and robustness of the proposed method under various conditions.

Deep learning techniques have attracted much attention in the radar automatic target recognition. In this paper, we investigate an acceleration method of the convolutional neural network (CNN) on the field-programmable gate array (FPGA) for the embedded application of the millimeter-wave (mmW) radar-based human activity classification. Considering the micro-Doppler effect caused by a person's body movements, the spectrogram of mmW radar echoes is adopted as the CNN input. After that, according to the CNN architecture and the properties of the FPGA implementation, several parallel processing strategies are designed as well as data quantization and optimization of classification decision to accelerate the CNN execution. Finally, comparative experiments and discussions are carried out based on a measured dataset of nine individuals with four different actions by using a 77-GHz mmW radar. The results show that the proposed method not only maintains the high classification accuracy but also improves its execution speed, memory requirement, and power consumption. Specifically, compared with the implementation of the same network model on a graphics processing unit, it could achieve the speedup of about 30.42% at the cost of the classification accuracy loss of only 0.27%.

Feature representation based on the high resolution range profile (HRRP) is important in radar automatic target recognition(RATR). Traditional algorithms of feature extraction utilize hallow architectures and rarely address the challenges of high-noise and unknown-noise distribution. The capability of RATA is restricted by these challenges. In this paper, a novel blind-denoising network(BDNet) is proposed to implement denoising and automatically extract features. As an extension of deep autoencoder, BDNet is based on fully convolutional architecture and employs fusion layers to transfer input features to high dimensional space. Trained with noise-to-noise, BDNet can implement blind-denoising and doesn't rely on noise distribution. Then the output of BDNet is used to classify the targets. In the experiment, we use the measured HRRP signals of four aircrafts to show the effectiveness of our methods. The results prove that BDNet can achieve blind-denoising in highnoise environment and significantly improve the performance of recognition.Andourproposed BDNetAlexNet outperforms other recognition methods.

Micro-Doppler (m-D) signature based target classification methods have received the intensive attention from the radar automatic target recognition (RATR) community. Since the m-D signals of a coning target with micro-motion are always multicomponent, it is important to separate these m-D frequency components for feature extraction and parameter estimation. This paper develops a separation algorithm based on multiple target tracking (MTT) technology, where the m-D frequency curves are assumed to be the tracks of maneuvering targets. The proposed method shows the good performance on the electromagnetic computation data in the experiments.