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.

High resolution range profile analysis based on multicarrier phase-coded waveforms of OFDM radar

Orthogonal frequency division multiplexing（OFDM） radar with multicarrier phase-coded waveforms has been recently introduced to achieve high range resolution.The conventional method for obtaining the high resolution range profile（HRRP） is based on matched filters.A method of synthesizing HRRP based on the fast Fourier transform（FFT） and decoding is proposed.The mathematical expressions of HRRP are derived by assuming an elementary scenario of point-scattering targets.Based on the characteristic of OFDM multicarrier signals,it mainly analyzes the influence on HRRP exerted by several factors,such as velocity compensation errors,the sampling frequency offset,and so on.The conclusions are significant for the design of the OFDM imaging radar.Finally,the simulation results demonstrate the validity of the conclusions.

Length estimation of extended targets based on bistatic high resolution range profile

The approach to estimate the length of extended targets by using the bistatic high resolution range profile（ H RRP） is analyzed in this paper. The relationship between the bistatic H RRP and the monostatic H RRP of extended targets are investigated. It is demonstrated by simulations that the target length measured by the bistatic H RRP is more meaningful and accurate than that by the monostatic HRRP,though the monostatic H RRP has been well developed and widely used in target recognizing and classification. The estimation results of a cone shaped target are present and compared at the end of the paper. To assure the reliability of the simulation,the bistatic H RRP is obtained through the scattering field data calculated by a fullwave numerical method,FE-BI-MLFMA.

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.

Multichannel noncoherent integration detection using high range resolution profile

A multichannel noncoherent integration detection method based on high range resolution profile was presented in this paper. According to the property of the moment generating function, the distribution characteristics of the noncoherent integrated signals with or without target presence were derived under the circumstance with noncorrelated Gaussian distribution noises. The loss of noncoherent integration was due to improper selection of integration range of cell numbers. A multi channel noncoherent integration detection scheme where the integration number in each channel va ries was proposed to solve this problem. The quality of this method for detection of various targets was evaluated. A comparison of fixed integration range cell number detection and multichannel inte gration detection for a high range resolution profile was presented. Simulation results indicated that the principle of the method was correct and performed well for unknown physical dimension targets. The method required little prior knowledge about target and was convenient for practical implementa tion.

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.

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.

Simutaneous radar imaging and velocity measuring with step frequency waveforms

The principle and method of both radar target imaging and velocity measurement simultaneously based on step frequency waveforms is presented. Velocity compensation is necessary in order to obtain the good High resolution range profile since this waveform is greatly sensitive to the Doppler shift. The velocity measurement performance of the four styles is analyzed with two pulse trains consisted of positive and negative step frequency waveforms. The velocity of targets can be estimated first coarsely by using the pulse trains with positive-positive step frequency combination, and then fine by positive-negative combination. Simulation results indicate that the method can accomplish the accurate estimation of the velocity with efficient computation and good anti-noise performance and obtain the good HRRP simultaneously.

基于稀疏表示的高分辨距离像自动目标识别(英文)

Sparse representation is a new signal analysis method which is receiving increasing attention in recent years. In this article, a novel scheme solving high range resolution profile automatic target recognition for ground moving targets is proposed. The sparse representation theory is applied to analyzing the components of high range resolution profiles and sparse coefficients are used to describe their features. Numerous experiments with the target type number ranging from 2 to 6 have been implemented. Results show that the proposed scheme not only provides higher recognition preciseness in real time, but also achieves more robust performance as the target type number increases.

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.

Method for denoising and reconstructing radar HRRP using modified sparse auto-encoder

A high resolution range profile(HRRP) is a summation vector of the sub-echoes of the target scattering points acquired by a wide-band radar.Generally, HRRPs obtained in a noncooperative complex electromagnetic environment are contaminated by strong noise.Effective pre-processing of the HRRP data can greatly improve the accuracy of target recognition.In this paper, a denoising and reconstruction method for HRRP is proposed based on a Modified Sparse Auto-Encoder, which is a representative non-linear model.To better reconstruct the HRRP, a sparse constraint is added to the proposed model and the sparse coefficient is calculated based on the intrinsic dimension of HRRP.The denoising of the HRRP is performed by adding random noise to the input HRRP data during the training process and fine-tuning the weight matrix through singular-value decomposition.The results of simulations showed that the proposed method can both reconstruct the signal with fidelity and suppress noise effectively, significantly outperforming other methods, especially in low Signal-to-Noise Ratio conditions.