An Aerial Target Recognition Algorithm Based on Self-Attention and LSTM

In the application of aerial target recognition,on the one hand,the recognition error produced by the single measurement of the sensor is relatively large due to the impact of noise.On the other hand,it is difficult to apply machine learning methods to improve the intelligence and recognition effect due to few or no actual measurement samples.Aiming at these problems,an aerial target recognition algorithm based on self-attention and Long Short-Term Memory Network(LSTM)is proposed.LSTM can effectively extract temporal dependencies.The attention mechanism calculates the weight of each input element and applies the weight to the hidden state of the LSTM,thereby adjusting the LSTM’s attention to the input.This combination retains the learning ability of LSTM and introduces the advantages of the attention mechanism,making the model have stronger feature extraction ability and adaptability when processing sequence data.In addition,based on the prior information of the multidimensional characteristics of the target,the three-point estimation method is adopted to simulate an aerial target recognition dataset to train the recognition model.The experimental results show that the proposed algorithm achieves more than 91%recognition accuracy,lower false alarm rate and higher robustness compared with the multi-attribute decision-making(MADM)based on fuzzy numbers.

Underwater Noise Target Recognition Based on Sparse Adversarial Co-Training Model with Vertical Line Array

The automatic identification of underwater noncooperative targets without label records remains an arduous task considering the marine noise interference and the shortage of labeled samples.In particular,the data-driven mechanism of deep learning cannot identify false samples,aggravating the difficulty in noncooperative underwater target recognition.A semi-supervised ensemble framework based on vertical line array fusion and the sparse adversarial co-training algorithm is proposed to identify noncooperative targets effectively.The sound field cross-correlation compression(SCC)feature is developed to reduce noise and computational redundancy.Starting from an incomplete dataset,a joint adversarial autoencoder is constructed to extract the sparse features with source depth sensitivity,aiming to discover the unknown underwater targets.The adversarial prediction label is converted to initialize the joint co-forest,whose evaluation function is optimized by introducing adaptive confidence.The experiments prove the strong denoising performance,low mean square error,and high separability of SCC features.Compared with several state-of-the-art approaches,the numerical results illustrate the superiorities of the proposed method due to feature compression,secondary recognition,and decision fusion.

Air target recognition method against ISRJ for radio frequency proximity sensors using chaotic stream encryption

The interrupted-sampling repeater jamming(ISRJ)can cause false targets to the radio-frequency proximity sensors(RFPSs),resulting in a serious decline in the target detection capability of the RFPS.This article proposes a recognition method for RFPSs to identify the false targets caused by ISRJ.The proposed method is realized by assigning a unique identity(ID)to each RFPS,and each ID is a periodically and chaotically encrypted in every pulse period.The processing technique of the received signal is divided into ranging and ID decryption.In the ranging part,a high-resolution range profile(HRRP)can be obtained by performing pulse compression with the binary chaotic sequences.To suppress the noise,the singular value decomposition(SVD)is applied in the preprocessing.Regarding ID decryption,targets and ISRJ can be recognized through the encryption and decryption processes,which are controlled by random keys.An adaptability analysis conducted in terms of the peak-to-side lobe ratio(PSLR)and bit error rate(BER)indicates that the proposed method performs well within a 70-k Hz Doppler shift.A simulation and experimental results show that the proposed method achieves extremely stable target and ISRJ recognition accuracies at different signal-to-noise ratios(SNRs)and jamming-to-signal ratios(JSRs).

New Algorithm for Image Target Recognition Based on Fractal Feature Fusion

By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Comparison and simulation are performed on the new algorithm, the old algorithm based on single feature and the algorithm based on neural network. Results of the comparison and simulation illustrate that the new algorithm is feasible and valid.

OPTIMIZATION OF WEIGHTED HIGH-RESOLUTION RANGE PROFILE FOR RADAR TARGET RECOGNITION

For the recognition of high-resolution range profile （HRRP） in radar, the weighted HRRP can reduce the instability of range cells caused by the attitude change of targets. A novel approach is proposed to optimize the weighted HRRP. In the approach, the separability of weighted HRRPs in different targets is measured by de- signing an objective function, and the weighted coefficients are computed by using the gradient descent method, thus enhancing the influence of stable range cells. Simulation results based on five aircraft models show that the approach can effectively optimize the weighted HRRP and improve the recognition accuracy.

Target recognition based on modified combination rule

Evidence theory is widely used in the field of target recognition. The invalidation problem of this theory when dealing with highly conflict evidences is a research hotspot. Several alternatives of the combination rule are analyzed and compared. A new combination approach is proposed. Calculate the reliabilities of evidence sources using existing evidences. Construct reliabilities judge matrixes and get the weights of each evidence source. Weight average all inputted evidences. Combine processed evidences with D-S combination rule repeatedly to identify a target. The application in multi-sensor target reeognition as well as the comparison with typical alternatives all validated that this approach can dispose highly conflict evidences efficiently and get reasonable reeognition results rapidly.

Radar group target recognition based on HRRPs and weighted mean shift clustering

When range high-resolution radar is applied to target recognition,it is quite possible for the high-resolution range profiles(HRRPs)of group targets in a beam to overlap,which reduces the target recognition performance of the radar.In this paper,we propose a group target recognition method based on a weighted mean shift(weighted-MS)clustering method.During the training phase,subtarget features are extracted based on the template database,which is established through simulation or data acquisition,and the features are fed to the support vector machine(SVM)classifier to obtain the classifier parameters.In the test phase,the weighted-MS algorithm is exploited to extract the HRRP of each subtarget.Then,the features of the subtarget HRRP are extracted and used as input in the SVM classifier to be recognized.Compared to the traditional group target recognition method,the proposed method has the advantages of requiring only a small amount of computation,setting parameters automatically,and having no requirement for target motion.The experimental results based on the measured data show that the method proposed in this paper has better recognition performance and is more robust against noise than other recognition methods.

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.

Spin-image surface matching based target recognition in laser radar range imagery

We explore the problem of in-plane rotation-invariance existing in the vertical detection of laser radar （Ladar） using the algorithm of spin-image surface matching. The method used to recognize the target in the range imagery of Ladar is time-consuming, owing to its complicated procedure, which violates the requirement of real-time target recognition in practical applications. To simplify the troublesome procedures, we improve the spin-image algorithm by introducing a statistical correlated coeff^cient into target recognition in range imagery of Ladar. The system performance is demonstrated on sixteen simulated noise range images with targets rotated through an arbitrary angle in plane. A high efficiency and an acceptable recognition rate obtained herein testify the validity of the improved algorithm for practical applications. The proposed algorithm not only solves the problem of in-plane rotation-invariance rationally, but also meets the real-time requirement. This paper ends with a comparison of the proposed method and the previous one.

Summed volume region selection based three-dimensional automatic target recognition for airborne LIDAR

Airborne LIDAR can flexibly obtain point cloud data with three-dimensional structural information,which can improve its effectiveness of automatic target recognition in the complex environment.Compared with 2D information,3D information performs better in separating objects and background.However,an aircraft platform can have a negative influence on LIDAR obtained data because of various flight attitudes,flight heights and atmospheric disturbances.A structure of global feature based 3D automatic target recognition method for airborne LIDAR is proposed,which is composed of offline phase and online phase.The performance of four global feature descriptors is compared.Considering the summed volume region(SVR) discrepancy in real objects,SVR selection is added into the pre-processing operations to eliminate mismatching clusters compared with the interested target.Highly reliable simulated data are obtained under various sensor’s altitudes,detection distances and atmospheric disturbances.The final experiments results show that the added step increases the recognition rate by above 2.4% and decreases the execution time by about 33%.

New statistical model for radar HRRP target recognition

The mixture of factor analyzers （MFA） can accurately describe high resolution range profile （HRRP） statistical charac- teristics. 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 indepen- dent. During computing the parameters of the model, birth-death moves are utilized to determine the optimal number of model au- tomatically. Experimental results for measured data show that the VBMFA method has better recognition performance than FA and MFA method.

Automatic target recognition of moving target based on empirical mode decomposition and genetic algorithm support vector machine

In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.

A Target Recognition Approach to Projecting HRR Profiles onto Subspace

Abstract: A array of the azimuthally averaged range-profile vectors and the inter-class and intra-class divergence matrixesare constructed iwth many frames of the high resolution range profiles which result from radar echoes of airplanes. Takingthe methods of whitening transformation and SVD produces a system of subspace vectors for target recognition. Where-upon, a template library for target recognition is built by the projection of a class-mean vector made from the radar dataonto the subspace for recognition. By Euclidean distance, a comparison is made between the above projection and eachtemplate in the library, to decide which class the target belongs to. Finally, simulations with the experimental radar dataarte given to show that the proposed method is robust to variation in azimuth and immune to additive Gaussian noisewhen SNR≥5dB.

SIMULTANEOUS MULTIPLE TARGET RECOGNITION USING POLARIZATION AGILE WAVES

A novel matching method for simultaneous multi-target recognition is proposed by jointly considering target's prior scattering knowledge and the polarization parameters of radar echoes. The matching coefficients are calculated for the judgment. MATLAB simulations show that several targets can be accurately recognized simultaneously, and a high recognition probability can be achieved in Monte Carlo simulations. The total execution time can be remarkably reduced in the Field Programmable Gate Array (FPGA) implementation of the matching procedure.

LLE-BASED CLASSIFICATION ALGORITHM FOR MMW RADAR TARGET RECOGNITION

In this paper,a new multiclass classification algorithm is proposed based on the idea of Locally Linear Embedding(LLE),to avoid the defect of traditional manifold learning algorithms,which can not deal with new sample points.The algorithm defines an error as a criterion by computing a sample's reconstruction weight using LLE.Furthermore,the existence and characteristics of low dimensional manifold in range-profile time-frequency information are explored using manifold learning algorithm,aiming at the problem of target recognition about high range resolution MilliMeter-Wave(MMW) radar.The new algorithm is applied to radar target recognition.The experiment results show the algorithm is efficient.Compared with other classification algorithms,our method improves the recognition precision and the result is not sensitive to input parameters.

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.

UWB radar target recognition based on time-domain bispectrum

Complex targets are irradiated by UWB radar, not only the mirror scattering echoes but also the multiscattering interacting echoes are included in target echoes. These two echoes can not be distinguished by classical frequency spectrum and power spectrurm. Time-domain bispectrum features of UWB radar signals that mingled with noise are analyzed, then processing this kind of signal using the method of time-domain bispectrum is experimented. At last, some UW-B radar returns with different signal noise ratio are simulated using 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.

ATMS Based Information Fusion Target Recognition Method

The non-monotonic problem exited in information fusion systems is solved. Through the introducing of non-monotonic reasoning method, which was realized with ATMS, into the information fusion system, it gains the ability to process insufficient information with flexibility and non-monotonic behavior. In the simulation test of our system, our system manifests its ability of dealing the insufficient and contradictory information, which partly solves the decision dilemma brought out by the insufficient information in battle situations. The information fusion target recognition system can process the information in battle situation fast and with flexibility.

Threshold Filtering Semi-Supervised Learning Method for SAR Target Recognition

The semi-supervised deep learning technology driven by a small part of labeled data and a large amount of unlabeled data has achieved excellent performance in the field of image processing.However,the existing semisupervised learning techniques are all carried out under the assumption that the labeled data and the unlabeled data are in the same distribution,and its performance is mainly due to the two being in the same distribution state.When there is out-of-class data in unlabeled data,its performance will be affected.In practical applications,it is difficult to ensure that unlabeled data does not contain out-of-category data,especially in the field of Synthetic Aperture Radar(SAR)image recognition.In order to solve the problem that the unlabeled data contains out-of-class data which affects the performance of the model,this paper proposes a semi-supervised learning method of threshold filtering.In the training process,through the two selections of data by the model,unlabeled data outside the category is filtered out to optimize the performance of the model.Experiments were conducted on the Moving and Stationary Target Acquisition and Recognition(MSTAR)dataset,and compared with existing several state-of-the-art semi-supervised classification approaches,the superiority of our method was confirmed,especially when the unlabeled data contained a large amount of out-of-category data.

The research on high speed underwater target recognition based on fuzzy logic inference

The underwater target recognition is a key technology in acoustic confrontation and underwater defence. In this article, a recognition system based on fuzzy logic inference (FLI) is set up. This system is mainly composed of three parts: the fuzzy input module, the fuzzy logic inference module with a set of inference rules and the de-fuzzy output module. The inference result shows the recognition system is effective in most conditions.