Radar high resolution range profile recognition via multi-SV method

For radar high resolution range profile(HRRP) recognition, three aspects are of great importance to improve the performance, i.e. discrimination for outlier, classification for inner and an accurate description for feature space. To tackle these issues, a novel target recognition method is designed, denoted by the multiple support vectors(multi-SV) method. With the proposed method,a special framework is constructed by a treble correlate support vector model to segment the feature space to two regions with the distribution of density, and then the description and classification hyperplane for each region are achieved. Based on the support vector framework, this method needs less memory and computation complexity to fit practical radar HRRP recognition. Finally,the experiment based on the measured data verifies the excellent performance of this method.

Radar emitter multi-label recognition based on residual network

In low signal-to-noise ratio(SNR)environments,the traditional radar emitter recognition(RER)method struggles to recognize multiple radar emitter signals in parallel.This paper proposes a multi-label classification and recognition method for multiple radar-emitter modulation types based on a residual network.This method can quickly perform parallel classification and recognition of multi-modulation radar time-domain aliasing signals under low SNRs.First,we perform time-frequency analysis on the received signal to extract the normalized time-frequency image through the short-time Fourier transform(STFT).The time-frequency distribution image is then denoised using a deep normalized convolutional neural network(DNCNN).Secondly,the multi-label classification and recognition model for multi-modulation radar emitter time-domain aliasing signals is established,and learning the characteristics of radar signal time-frequency distribution image dataset to achieve the purpose of training model.Finally,time-frequency image is recognized and classified through the model,thus completing the automatic classification and recognition of the time-domain aliasing signal.Simulation results show that the proposed method can classify and recognize radar emitter signals of different modulation types in parallel under low SNRs.

Radar Signal Recognition by CWD Picture Features

In this paper a system for automatic recognition of radar waveform is introduced. This technique is used in many spectrum management, surveillance, and cognitive radio and radar applications. For instance the transmitted radar signal is coded into six codes based on pulse compression waveform such as linear frequency modulation (LFM), Frank code, P1, P2, P3 and P4 codes, the latter four are poly phase codes. The classification system is based on drawing Choi Willliams Distribution (CWD) picture and extracting features from it. In this study, various new types of features are extracted from CWD picture and then a pattern recognition method is used to recognize the spectrum. In fact, signals from CWD picture are defined using biometric techniques. We also employ false reject rate (FRR) and false accept rate (FAR) which are two types of fault measurement criteria that are deploy in biometric papers. Fairly good results are obtained for recognition of Signal to Noise Ratio (-11 dB).

Adaptive target and jamming recognition for the pulse doppler radar fuze based on a time-frequency joint feature and an online-updated naive bayesian classifier with minimal risk

This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed.Then,the frequency entropy and peak-to-peak ratio are extracted from the matched filter output of the PDRF,and the time-frequency joint feature is constructed.Based on the time-frequency joint feature,the naive Bayesian classifier(NBC)with minimal risk is established for target and jamming recognition.To improve the adaptability of the proposed method in complex environments,an online update process that adaptively modifies the classifier in the duration of the work of the PDRF is proposed.The experiments show that the PDRF can maintain high recognition accuracy when the signal-to-noise ratio(SNR)decreases and the jamming-to-signal ratio(JSR)increases.Moreover,the applicable analysis shows that he ONBCMR method has low computational complexity and can fully meet the real-time requirements of PDRF.

Human Activity Recognition Based on Frequency-Modulated Continuous Wave and DenseNet

With the development of wireless technology, Frequency-Modulated Continuous Wave (FMCW) radar has increased sensing capability and can be used to recognize human activity. These applications have gained wide-spread attention and become a hot research area. FMCW signals reflected by target activity can be collected, and human activity can be recognized based on the measurements. This paper focused on human activity recognition based on FMCW and DenseNet. We collected point clouds from FMCW and analyzed them to recognize human activity because different activities could lead to unique point cloud features. We built and trained the neural network to implement human activities using a FMCW signal. Firstly, this paper presented recent reviews about human activity recognition using wireless signals. Then, it introduced the basic concepts of FMCW radar and described the fundamental principles of the system using FMCW radar. We also provided the system framework, experiment scenario, and DenseNet neural network structure. Finally, we presented the experimental results and analyzed the accuracy of different neural network models. The system achieved recognition accuracy of 100 percent for five activities using the DenseNet. We concluded the paper by discussing the current issues and future research directions.

Algorithm for Gesture Recognition Using an IR-UWB Radar Sensor

This paper introduces a human gesture recognition algorithm using an impulse radio ultra-wide- band (IR-UWB) radar sensor. Human gesture recognition has been one of the hottest research topics for quite a long time. Many gesture recognition algorithms or systems using other sensors have been proposed such as using cameras, RFID tags and so on. Among which gesture recognition systems using cameras have been extensively studied in past years and widely used in practical. While it might show some deficiencies in some cases. For example, the users might not like to be filmed by cameras considering their privacies. Besides, it might not work well in very dark environments. While RFID tags could be inconvenient to many people and are likely to be lost. Our gesture recognition algorithm uses IR-UWB radar sensor which has pretty high resolution in ranging and adjustable gesture recognition range, meanwhile, does not have problems in privacy issues or darkness. In this paper, the gesture recognition algorithm is based on the moving direction and distance change of the human hand and the change of the frontal surface area of hand towards radar sensor. By combining these changes while doing gestures, the algorithm may recognize basically 6 kinds of hand gestures. The experimental results show that these gestures are of quite good performance. The performance analysis from experiments is also given.

Modulation recognition of MIMO radar signal based on joint HOS and SNR algorithm

This paper presents a joint high order statistics(HOS)and signal-to-noise ratio(SNR) algorithm for the recognition of multiple-input multiple-output(MIMO) radar signal without a priori knowledge of the signal parameters. This method is capable of recognizing the MIMO radar signal as well as discriminating it from single-carrier signal adopted by conventional radar. Meanwhile,the sub-carrier number of the none-coding MIMO radar signal is estimated. Extensive simulations are carried out in different operating conditions. Simulation results prove the feasibility and indicate that the recognition probability could reach over 90% when the value of SNR is above 0 dB.

Key Radar Signal Sorting and Recognition Method Based on Clustering Combined with PRI Transform Algorithm

In this paper,we investigate the problem of key radar signal sorting and recognition in electronic intelligence(ELINT).Our major contribution is the development of a combined approach based on clustering and pulse repetition interval(PRI)transform algorithm,to solve the problem that the traditional methods based on pulse description word(PDW)were not exclusively targeted at tiny particular signals and were less time-efficient.We achieve this in three steps:firstly,PDW presorting is carried out by the DBSCAN(Density-Based Spatial Clustering of Applications with Noise)clustering algorithm,and then PRI estimates of each cluster are obtained by the PRI transform algorithm.Finally,by judging the matching between various PRI estimates and key targets,it is determined whether the current signal contains key target signals or not.Simulation results show that the proposed method should improve the time efficiency of key signal recognition and deal with the complex signal environment with noise interference and overlapping signals.

A New Radar Target Recognition Method Based on Polarimetric Processing and Neural Learning

The new millimeter-wave(MMW) radar target recognition method proposed uses polarmetric information to obtain stable amplitudes of range profiles and neural learning to extract angle-invariant features of range profiles and polarimetric processing reduces speckle to enhance ability to discriminate targets, and in comparison with conventional approaches, subclass features obtained by the neural learning carries more information and thus makes the correctness of target classification higher and simulation results vended the validity of this approach.

RELATIONSHIP OF TARGET RECOGNITION PERFORMANCE AND RADAR WAVEFORM PARAMETERS

Target recognition performance can be affected by radar waveform parameters.In this paper,we established rigorous relationship between target recognition efficiency and the parameters of a repeatedly transmitted waveform.It is based on Kullback-Leibler Information Number of single observation(KLINs),which measures the dissimilarity between targets depicted by a range-velocity double spread density function in frequency domain.We considered two signal models which are different in the coherence of the observations.The method we proposed takes advantage of the methodology of sequential hypothesis test,and then the recognition performance in terms of correct classification rate is expressed by Receiver Operating Characteristic(ROC).Simulation results about the parameters of LFM signal show the validity of the method.

New statistical model for radar HRRP target recognition

The mixture of factor analyzers(MFA) can accurately describe high resolution range profile(HRRP) statistical characteristics.But how to determine the proper number of the models is a problem.This paper develops a variational Bayesian mixture of factor analyzers(VBMFA) model.This procedure can obtain a lower bound on the Bayesian integral using the Jensen's inequality. An analytical solution of the Bayesian integral could be obtained by a hypothesis that latent variables in the model are independent.During computing the parameters of the model,birth-death moves are utilized to determine the optimal number of model automatically.Experimental results for measured data show that the VBMFA method has better recognition performance than FA and MFA method.

UWB radar target recognition based on time-domain bispectrum

Complex targets are irradiated by UWB radar, n ot only the mirror scattering echoes but also the multi-scattering interacting ec hoes are included in target echoes. These two echoes can not be distinguished by classical frequency spectrum and power spectrum. Time-domain bispectrum featur es of UWB radar signals that mingled with noise are analyzed, then processing th is kind of signal using the method of time-domain bispectrum is experimented. A t last, some UWB radar returns with different signal noise ratio are simulated u sing the method of time-domain bispectrum. Theoretical analysis and the results of simulation show that the method of extraction partial features of UWB radar targets based on time-domain bispectrum is good, and target classification and recognition can be implemented using those features.

Human Motion Recognition Using Ultra-Wideband Radar and Cameras on Mobile Robot

Cameras can reliably detect human motions in a normal environment,but they are usually affected by sudden illumination changes and complex conditions,which are the major obstacles to the reliability and robustness of the system.To solve this problem,a novel integration method was proposed to combine bi-static ultra-wideband radar and cameras.In this recognition system,two cameras are used to localize the object's region,regions while a radar is used to obtain its 3D motion models on a mobile robot.The recognition results can be matched in the 3D motion library in order to recognize its motions.To confirm the effectiveness of the proposed method,the experimental results of recognition using vision sensors and those of recognition using the integration method were compared in different environments.Higher correct-recognition rate is achieved in the experiment.

FEATURE EXTRACTION AND RECOGNITION FOR ECHOES OF HRR RADAR

This paper describes a novel target recognition scheme using High Range Resolution (HRR) radar signatures. AutoRegressive (AR) method is used to extract features from HRR radar echoes based on scattering center model of target. The optimal linear transformation based on Euclidian distribution distance criterion is performed on AR model parameter vectors to reduce dimension of feature vectors further and improve the class discrimination capability of feature vectors. The optimization algorithm is designed utilizing the quadratic property of criterion function and Gaussian kernel based Parzen window density function estimator. The concept of Stochastic Information Gradient (SIG) is incorporated into the gradient of cost function to decrease the computational complexity of the algorithm. Simulation results using three real airplanes,data show the effectiveness of the proposed method.

SAR target recognition based on active contour without edges

A new method for synthetic aperture radar(SAR) target recognition is proposed. This method is accomplished via the combination of active contour without edges, Hu invariant moments and support vector machine(SVM) classifier. Image segmentation is performed by using active contour without edges. Then seven Hu moments are extracted and normalized as feature vectors. Finally, the SVM classifier is employed for data training and testing by means of MSTAR SAR images. To verify the performance of the proposed method, the traditional active contour(snakes) is used for comparison. The simulation results confirm the feasibility and accuracy of the proposed method in SAR target recognition.

Research on PCA and KPCA Self-Fusion Based MSTAR SAR Automatic Target Recognition Algorithm

This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis (PCA) and nonlinear feature extracted from kernel principal component analysis (KPCA) respectively, and then utilizes the adaptive feature fusion algorithm which is based on the weighted maximum margin criterion (WMMC) to fuse the features in order to achieve better performance. The linear regression classifier is used in the experiments. The experimental results indicate that the proposed self-fusion algorithm achieves higher recognition rate compared with the traditional PCA and KPCA feature fusion algorithms.

THE INFLUENCE OF SPECKLE ON HIGH RESOLUTION RANGE PROFILE RECOGNITION BASED ON THE MATCHING SCORE

Template database is the key to radar automation target recognition based on High Resolution Range Profile (HRRP). From the traditional perspective, average HRRP is a valid template for it can represent each HRRP without scatterer Moving Through Range Cell (MTRC). However, template database based on this assumption is always challenged by measured data. One reason is that speckle happens in the frame without scatterer MTRC. Speckle makes HRRP fluctuate sharply and not match well with the average HRRP. We precisely introduce the formation mechanism of speckle. Then, we make an insight into the principle of matching score. Based on the conclusion, we study the properties of matching score between speckled HRRP and the average HRRP. The theoretical analysis and Monte Carlo experimental results demonstrate that speckle makes HRRP not to match well with the average HRRP according to the energy ratio of speckled scatterers. On the assumption of ideal scattering centre model, speckled HRRP has a matching score less than 85% with the average HRRP if speckled scatterers occupy more than 50% energy of the target.

Deep Feature Bayesian Classifier for SAR Target Recognition with Small Training Set

In recent years,deep learning algorithms have been popular in recognizing targets in synthetic aperture radar(SAR)images.However,due to the problem of overfitting,the performance of these models tends to worsen when just a small number of training data are available.In order to solve the problems of overfitting and an unsatisfied performance of the network model in the small sample remote sensing image target recognition,in this paper,we uses a deep residual network to autonomously acquire image features and proposes the Deep Feature Bayesian Classifier model(RBnet)for SAR image target recognition.In the RBnet,a Bayesian classifier is used to improve the effect of SAR image target recognition and improve the accuracy when the training data is limited.The experimental results on MSTAR dataset show that the RBnet can fully exploit effective information in limited samples and recognize the target of the SAR images more accurately.Compared with other state-of-the-art methods,our method offers significant recognition accuracy improvements under limited training data.Noted that theRBnet is moderately difficult to implement and has the value of popularization and application in engineering application scenarios in the field of small-sample remote sensing target recognition and recognition.

A method for coastal oil tank detection in polarimetric SAR images based on recognition of T-shaped harbor

To automatically detect oil tanks in polarimetric synthetic aperture radar(SAR) images, a coastal oil tank detection method is proposed based on recognition of T-shaped harbor. First of all, the T-shaped harbor is detected to locate the region of interest(ROI) of oil tanks. Then all suspicious targets in the ROI are extracted by the segmentation of strong scattering targets and the classifier of H/α. The template targets are selected from the suspicious targets by the combination of a proposed circular degree parameter and the similarity parameter(SP) of the polarimetric coherency matrix. Finally, oil tanks are detected according to the statistics of the similarity parameter between each suspicious target and template targets in ROI. Polarimetric SAR data acquired by RADARSAT-2 over Berkeley and Singapore areas are used for testing. Experiment results show that most of the targets are correctly detected and the overall detection rate is close to 80%.The false rate is effectively reduced by the proposed algorithm compared with the method without T-shaped harbor recognition.

ISAR target recognition based on non-negative sparse coding

Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar (ISAR) target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding (NNSC) and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis (PCA) and non-negative matrix factorization (NMF) via simulations.