Electromagnetic model (em-model) provides a concise and physically relevant description of target through representative scatterers. In a forward built em-model, detailed information about each scatterer’s position, scattering amplitude along with its provenance can be predicted. This makes em-model a good candidate for use in synthetic aperture radar (SAR) automatic target recognition (ATR). In this paper, we introduce scatterers’ provenance as attributed information into target recognition, and an attributed em-model based target recognition method is proposed. Firstly, according to the purpose of ATR, each scatterer in em-model is endowed with an importance factor based on its provenance. Secondly, a detection is implemented to decide whether the em-model predicted scatterer has a corresponding scatterer in measured data. If the scatterer exist in measured target, evaluate how similar the scatterer pair resembled with each other. Next, similarities of all the scatterer pairs are synthesized as a whole match score between em-model and SAR data. In the synthesis, the importance factor servers as a weighting factor that scatterer with more attention will be more discriminative for recognition. In the end, target in measured SAR data is recognized as the model type or not based on the match score. The novelty of this method comes from taking into account of the provenance information of scatterers as attributed information and endowing the scatterers with different important factors according to their importance in recognition. This makes the attributed scatterer based recognition method pertinent to the purpose of ATR. Experiments on simulated Tank SAR data that produced by a high frequency electromagnetic simulation software verified the effectiveness of this method.
A new partially occluded target location method based on straight line is proposed. It is divided into four steps: firstly, we label the straight lines of concerned target in the history image artificially and store the line points together with the grads orientation. The labeled lines, the length of which is restricted, should distribute symmetrically. Then, transform the stored lines using the transformation model whose parameters are derived from geometry calibration result of the real-time image. Afterwards, construct pyramid structure of real-time image and search the optimal match position. The geometry coherence rule is used to gain holistic optimal match result. Lastly, compare the matching measure with the threshold to decide whether need to perform the same match process using the higher solution image, and output the match result. The experiment results, tested by real-time remote sensing images especially when part of them are occluded, are shown that the proposed algorithm for target location is accurate and effective.