Radar Target Recognition Based On HRRP And JEM Signal

With the tendency of modern battle to become more and more information-based and intelligent, radar automatic target recognition(RATR) techniques have received intensive attentions. The development of modern radar technology supplies many usable signal forms for RATR, and many new productions in the RATR field have come forth in recent years. As a result, RATR is striding to practical realization from theoretical study. This dissertation provides our researches for RATR from two aspects, i.e. wideband radar High-resolution range profile (HRRP) automatic target recognition and narrow band radar automatic classification.The main contents of this dissertation are summarized as follows.1. The research of HRRP automatic recognition based on wideband radar includes four parts:In the first part, we discuss the sensitivities of airplane targets with the variation of course, pitch and roll angle, the property of the HRRPs near the airplane target's broadside, and coherent apex phenomena in test data. Furthermore, due to the target-aspect sensitivity, we propose a recursive algorithm for adaptively angular sector segmenting which exploit the nonlinear structure characteristic embedded in HRRP data through classifiers that have probability meaning.The second part focuses on the sensitivities of amplitude-scale and time-shift in radar HRRP statistical recognition. Most approaches available just use the slide correlation processing or its modifications for time-shift sensitivity while simply normalizing the amplitude-scale. Obviously, approaches available solve the two sensitivity problems disjointedly, which leads to mismatch and limits recognition performance. In order to improve the matching precision, we propose two algorithms to jointly match the amplitude and time-shift, based on independent Gaussian model, PCA subspace and PPCA subspace statistical model. One of the algorithms is used in the training phase and the other one is used in the test phase.The third part presents a noise-robust adaptive statistical recognition method. We hope to recognize target at long distance in RATR, and therefore, the robustness study of recognition algorithm is necessary. In this part, based on PPCA model and FA model, a robust method for adaptive statistical recognition is presented when test SNR is lower than training SNR.The fourth part focuses on how to get data, learn and build the template database interactively and concurrently. We present two online methods. 1) An online mixture of experts (OME) is used to divide the HRRP data into several regions within which online Gaussian process classifier (OGPC) make predictions. Due to EM and EP, parameter learning methods for iterative online Gaussian process (IOGP), a Bi-online Gaussian process (Bi-OGP) is proposed to learn parameters by single pass of data. Due to EM algorithm for OME's parameter learning, a single data pass method based on proper initial values is presented. 2) With the assumption that returned echoes in range cells is independent Gaussian distributed, online parameters is first given, and then a two thresholds method is proposed to pick the outliers and reduce the mismatch between the statistics model and the online real HRRP data.2. Narrow band radar automatic classificationBecause narrow bandwidth signal target classification is a pre-processing method of HRRP RATR and the bandwidth of a lot of radar equipments in use is narrow, we study how to use Jet Engine Modulation (JEM) characteristic of low-resolution signal to categorize aeroplanes into three kinds, i.e., turbojet aircraft, prop aircraft and helicopter.Most low-resolution radar systems, especially ground surveillance radar systems, work at relatively low pulse repeat frequency (PRF) and with short time-on-target (TOT) (duration in scanning). Low PRF leads to Doppler ambiguity and short TOT results in low Doppler resolution, which poses a problem to target classification with low-resolution radar based on the JEM characteristic of radar echo. From the pattern classification viewpoint, using dispersion situations of JEM eigenvalue spectra, we propose a method for categorizing aeroplanes into three kinds, i.e., turbojet aircraft, prop aircraft and helicopter. We analyze the mathematical model of JEM echoes consisting of a series of line spectra and regard them as a sum of several series of harmonious waves. Classification features can be extracted based on the harmonious wave sum model. Some schemes for extracting features from echoes within or between pulses are proposed. Low-dimensional features are extracted to reduce computation burden. Our methods do not compensate for the fuselage echoes and are insensitive to the variation of fuselage Doppler.