Depolarization Degree to Determine Dihedral Attribute of Radar Target

This paper investigates the ability of the depolarization degree, derived from the characteristic polarization states at the resonant frequency set, to identify corner or swept, i.e. dihedral, changes in same-class targets by a metallic wire example. A well-estimated depolarization degree requires a robust extraction of the fundamental target resonance set in two orthogonal sets of fully co-polarized and cross-polarized polarization channels, then finding the null polarization states using the Lagrangian method. Such depolarization degree per resonance mode has the potential to form a robust feature set because it is relatively less sensitive to onset ambiguity, invariant to rotation, and could create a compact, recognizable, and separable distribution in the proposed feature space. The study was limited to two targets with two swept changes of fifteen degrees within normal incidence;under a supervised learning approach, the results showed that the identification rate converging to upper-bound (100%) for a signal-to-noise ratio above 20 dB and lower-bound around (50%) below −10 dB.

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.

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.

A New Rank Test Nonparametric Procedure for Radar Detection in Multiple Target Situations

This paper deals with the detection performance evaluation of a new nonparametric procedure. The test statistics of the proposed processor have a simple statistical form which is derived from that of the suboptimum rank test procedure [1]. The performance of the new distributionfree detection technique is obtained through Monte-Carlo simulations and compared with that of the suboptimum scheme when detecting nonfluctuating target embedded in Gaussian noise and in the presence of interfering target returns among the reference samples. The new nonparametric detector is shown to give a relative improvement over the suboptimum processor, especially, when the background environment contains a numerous-number of extraneous targets.

Techniques for Radar Imaging Based onMUSIC Algorithm

At first, the radar target scattering centers model and MUSIC algorithm are analyzed in this paper. How to efficiently set the parameters of the MUSIC algorithms is given by a great deal of simulated radar data in experiments. After that, according to measured data from two kinds of plane targets on fully polarized and high range resolution radar system, the author mainly investigated particular utilization of MUSIC algorithm in radar imaging. And two-dimensional radar images are generated for two targets measured in compact range. In the end, a conclusion is drew about the relation of radar target scattering properties and imaging results.

Radar Cross-Section Measurement Using the Near-Field Single-Frequency Angular-Diversity Technique

Radar cross-section (RCS) measurement with the near-field electromagnetic wave illumination of a target has been proved to be practical. The existing methods employ the multiplefrequency angular-diversity (MFAD) technique, whereas this paper considers the single-frequency angular-diversity (SFAD) technique. The paper takes into account the scattering center modeling and the limitation of higher sidelobes in reconstructing images in the SFAD technique compared to the MFAD technique. A method of combining the SFAD technique with the RELAX approach is presented for the high-resolution extraction of scattering centers on a target. The proposed method offers an excellent RCS recovery, which is validated by numerical results.

Application of Heterogeneous Multiple Feature Data Fusion to Multitarget Association and Tracking

In this paper we discuss a kind of multitarget tracking and association method based on the data fusion of heterogeneous multiple feature data gained by a sensor such as space state, signal amplitude, Doppler frequency and so on. In order to introduce quantitatively those heterogeneous multiple feature data which are possibly gained by a sensor into the discussion of tracking and association problem, we define a correlation measure which we explain as the generalization of conventional association decision. In conventional Nearest Neighbor method, the decision function can take only two values, 1 or 0, to represent the decision of association or not association. In our method, correlation measure can be take any real value from 0 to 1 to represent the extent of correlation. Considering the practical circumstances that some feature data might not be easily gained continuously, we introduce an effective factor to deal with these cases. In the paper we also discuss the comparative computer simulation tests and give the results.

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.

Feature Extraction of Radar Range Profiles Based on Normalized Central Moments

The normalized central moments are widely used in pattern recognition because of scale and translation invariance. The moduli of normalized central moments of the 1-dimensional complex range profiles are used here as feature vector for radar target recognition. The common feature extraction method for high resolution range profile obtained by using Fourier-modified direct Mellin transform is inefficient and unsatisfactory in recognition rate And. generally speaking, the automatic target recognition method based on inverse synthetic aperture radar 2-dimensional imaging is not competent for real time object identification task because it needs complicated motion compensation which is sometimes too difficult to carry out. While the method applied here is competent for real-time recognition because of its computational efficiency. The result of processing experimental data indicates that this method is good at recognition.

Robust radar automatic target recognition algorithm based on HRRP signature

Automatic target recognition （ATR） is an important function for modern radar. High resolution range profile （HRRP） of target contains target struc- ture signatures, such as target size, scatterer distribu- tion, etc, which is a promising signature for ATR. Sta- tistical modeling of target HRRPs is the key stage for HRRP statistical recognition, including model selection and parameter estimation. For statistical recognition al- gorithms, it is generally assumed that the test samples follow the same distribution model as that of the train- ing data. Since the signal-to-noise ratio （SNR） of the received HRRP is a function of target distance, the as- sumption may be not met in practice. In this paper, we present a robust method for HRRP statistical recogni- tion when SNR of test HRRP is lower than that of train- ing samples. The noise is assumed independent Gaus- sian distributed, while HRRP is modeled by probabilistic principal component analysis （PPCA） model. Simulated experiments based on measured data show the effective- ness of the proposed method.

Radar automatic target recognition based on feature extraction for complex HRRP

Radar high-resolution range profile （HRRP） has received intensive attention from the radar automatic target recognition （RATR） community. Usually, since the initial phase of a complex HRRP is strongly sensitive to target position variation, which is referred to as the initial phase sensitivity in this paper, only the amplitude information in the complex HRRP, called the real HRRP in this paper, is used for RATR, whereas the phase information is discarded. However, the remaining phase information except for initial phases in the complex HRRP also contains valuable target discriminant information. This paper proposes a novel feature extraction method for the complex HRRP. The extracted complex feature vector, referred to as the complex feature vector with difference phases, contains the difference phase information between range cells but no initial phase information in the complex HRRR According to the scattering center model, the physical mechanism of the proposed complex feature vector is similar to that of the real HRRP, except for reserving some phase information independent of the initial phase in the complex HRRP. The recognition algorithms, frame-template establishment methods and preprocessing methods used in the real HRRP-based RATR can also be applied to the proposed complex feature vector-based RATR. Moreover, the components in the complex feature vector with difference phases approximate to follow Gaussian distribution, which make it simple to perform the statistical recognition by such complex feature vector. The recognition experiments based on measured data show that the proposed complex feature vector can obtain better recognition performance than the real HRRP if only the cell interval parameters are properly selected.

A new feature extraction method using the amplitude fluctuation property of target HRRP for radar automatic target recognition

Due to the aspect sensitivity of high-resolution range profile(HRRP),traditional radar HRRP target recognition methods usually use average profile within some target-aspect region as the target-aspect template.Actually,the amplitude fluctuation property of target HRRP also represents some feature information of the target.Based on the scattering center model,a new feature extraction method using the amplitude fluctuation property of target HRRP is proposed in this paper.The weighted HRRP feature extracted by the new method can represent the scatterer distribution in every range cell,thereby it can describe the scattering property of the target better.The experimental results based on measured data show that the new feature extraction method can greatly improve recognition performances.

STUDY ON ALGORITHM OF SENSOR MANAGEMENT BASED ON FUNCTIONS OF EFFICIENCY AND WASTE

Sensor management plays an important role in data fusion system, and this paper presents an algorithm of sensor management that can be used in target detection, identification and tracking. First, the basic concept, rule, range and function of sensor management are introduced. Then, the quantifying problems of target priority and sensor (or combination)-target pairing in multisensor management are discussed and the efficiency and waste functions are established based on the functions of target priority and sensor-target pairing. On this basis, a distribution algorithm of multi-sensor resources is given, which is optimized by the principle of maximum synthesis efficiency in the multisensor system and constrained by sensor maximum tracking power and what target must be scanned. In addition, the waste measure of sensor resources is introduced to improve the algorithm. Finally, a tactical task that includes three sensors and ten targets is set, and the simulation results show that the algorithm is feasible and effective.

An Algorithm for Ship Wake Detection from the SAR Images Using the Radon Transform and Morphological Image Processing

Using the Radon transform and morphological image processing, an algorithm for ship's wake detection in the SAR (synthetic aperture radar) image is developed. Being manipulated in the Radon space to invert the gray-level and binary images, the linear texture of ship wake in oceanic clutter can be well detected. It has been applied to the automatic detection of a moving ship from the SEASAT SAR image. The results show that this algorithm is well robust in a strong noisy background and is not very sensitive to the threshold parameter and the working window size.

Radar HRRP statistical recognition with temporal factor analysis by automatic Bayesian Ying-Yang harmony learning

Radar high-resolution range profiles(HRRPs)are typical high-dimensional and interdimension dependently distributed data,the statistical modeling of which is a challenging task for HRRP-based target recognition.Supposing that HRRP samples are independent and jointly Gaussian distributed,a recent work[Du L,Liu H W,Bao Z.IEEE Transactions on Signal Processing,2008,56(5):1931–1944]applied factor analysis(FA)to model HRRP data with a two-phase approach for model selection,which achieved satisfactory recognition performance.The theoretical analysis and experimental results reveal that there exists high temporal correlation among adjacent HRRPs.This paper is thus motivated to model the spatial and temporal structure of HRRP data simultaneously by employing temporal factor analysis(TFA)model.For a limited size of high-dimensional HRRP data,the two-phase approach for parameter learning and model selection suffers from intensive computation burden and deteriorated evaluation.To tackle these problems,this work adopts the Bayesian Ying-Yang(BYY)harmony learning that has automatic model selection ability during parameter learning.Experimental results show stepwise improved recognition and rejection performances from the twophase learning based FA,to the two-phase learning based TFA and to the BYY harmony learning based TFA with automatic model selection.In addition,adding many extra free parameters to the classic FA model and thus becoming even worse in identifiability,the model of a general linear dynamical system is even inferior to the classic FA model.

Ballistic target recognition based on multiple data representations and deep-learning algorithms

Target recognition is a significant part of a Ballistic Missile Defense System(BMDS).However,most existing ballistic target recognition methods overlook the impact of data representation on recognition outcomes.This paper focuses on systematically investigating the influences of three novel data representations in the Range-Doppler(RD)domain.Initially,the Radar Cross Section(RCS)and micro-Doppler(m-D)characteristics of a cone-shaped ballistic target are analyzed.Then,three different data representations are proposed:RD data,RD sequence tensor data,and RD trajectory data.To accommodate various data inputs,deep-learning models are designed,including a two-Dimensional Residual Dense Network(2D RDN),a three-Dimensional Residual Dense Network-Gated Recurrent Unit(3D RDN-GRU),and a Dynamic Trajectory Recognition Network(DTRN).Finally,an Electromagnetic(EM)computation dataset is collected to verify the performances of the networks.A broad range of experimental results demonstrates the effectiveness of the proposed framework.Moreover,several key parameters of the proposed networks and datasets are extensively studied in this research.