Study Of Radar Automatic Target Recognition Methods Based On High Range Resolution Profile

Target high-resolution range profile (HRRP) represents the projection of the complex returned echoes from the target scattering centers onto the radar line-of-sight (LOS). It contains the target structure signatures, such as target size, scatterer distribution, etc., and thereby radar HRRP target recognition has received intensive attention from the radar automatic target recognition (RATR) community. Due to the increasing military demand, RATR are required to stride to practical realization from theoretical. In this dissertation, the theory and techniques for radar HRRP target recognition are researched from the three aspects, i.e. robust of HRRP recognition performance under the clutter and noise, the classifier designing based on variational Bayesian (VB), feature extraction and layered algorithm for target recognition, which are supported by Advanced Defense Research Programs of China and National Science Foundation of China.The main content of this dissertation is summarized as follows:The first part begins with a brief introduction of the fundamental theories of RATR and reviews some related work of other institutes. Then the main work of this thesis is introduced.The second part focuses on the robustness of HRRP recognition performance under the clutter environment. The key point is how to suppress clutter. Compared with the clutter suppression for target detection, clutter suppression for wideband target recognition radar requires that the target structure signatures are not changed after the clutter is suppressed. We present three methods of clutter suppression for wideband target recognition radar to achieve this purpose. (1) Clutter is suppressed by a filter in Doppler domain. This algorithm mainly exploits the fact that the velocity of clutter is small, and the correlation of clutter between different pulses is high. After the clutter suppression, we can transform the signal to the time domain, and then perform coherent accumulation, aiming at improving the signal-to-noise ratio (SNR) by. (2) In wideband radar, the target's migration though resolution cells (MTRCs) will occur when the velocity is high. But MTRC is not considered in Algorithm 1. Thereby, we utilize keystone formatting to mitigate the MTRCs, and then suppress clutter by algorithm1. Otherwise, if target has Doppler ambiguity while clutter does not, we extract the target directly to reduce the effect of clutter in Doppler-frequency domain. (3) If MTRC occurs, we can utilize the Hough transform to extract the line segment of target in Doppler-frequency domain even though MTRC is not mitigated via keystone formatting. A simple method is proposed if the velocity of target can be approximately estimated, which is extracting the line segment of target in Doppler-frequency domain after motion compensation.The third part is contributed to noise robust in HRRP target recognition. The SNR will be decreased when target is far away from radar, and therefore, the robustness study of HRRP recognition algorithm is necessary. In this part, based on PPCA model and AGC model, a robust algorithm for HRRP statistical recognition is presented when test SNR is lower than training SNR.The fourth part focuses on radar HRRP statistical recognition based on VB. VB method is widely used to approximately resolve Bayesian integral in recent decade. On the assumption that parameters and hidden variables are independent of each other, the jointly probability distribution over all parameters and hidden variables can be approximated with a simpler distribution which is a lower bound of original Bayesian integral. The lower bound is increased by optimizing parameters, and the aim is to approximate the real value of original Bayesian integral. We apply Gaussian mixture models and mixtures of factor analyzers model to radar HRRP statistical recognition based on VB method, and obtain a good performance with measured radar data.In the fifth part, utilizing the new feature extracting from HRRP, the layered radar target recognition is focused. Due to the fact that HRRP represents the projection of the complex returned echoes from the target scattering centers onto the radar line-of-sight (LOS), we extract target size, one of the target structure signatures. First, we utilize this feature to classify different targets by their size, and then identify them exactly by normal target recognition algorithm. In addition, we can distinguish propeller-driven aircraft from jet plane by relative difference energy between coherent echoes, because the relative difference energy between coherent echoes of propeller-driven aircraft is large than jet plane's.Finally, we summarize the main results of the study which have led to this thesis; additionally, some conclusions are drawn and some recommendations for future work are given.