Extracting precise target characteristics from microwave image is needed and calls for high-resolution microwave imaging radar systems. In this paper, a Ka-band ultra-wideband microwave photonic (MWP) imaging radar is developed and experimentally demonstrated. In the transmitter, continuous ultra-wideband linear frequency modulation (LFM) wave is generated based on optical frequency sextupling technique. In the receiver, a combination of optical frequency mixer with fiber delay lines and electric analog-to-digital converter (ADC) is capable of receiving target echoes and imaging targets with different distances. The maximum instantaneous bandwidth of the transmitted waveform is measured to be 10.02 GHz and corresponding range resolution is calibrated to be 1.68 cm. Out-field tests with demonstrator working at synthetic aperture radar (SAR) or inverse synthetic aperture radar (ISAR) mode are carried out. Different targets such as an unmanned aerial vehicle (UAV), airliner and Leifeng pagoda are imaged. Based on corresponding high-resolution microwave images, quantitative information of the targets can be identified, which shows the great potential of the radar demonstrator for various remote sensing applications.
-Dynamic frame fusion which is based on hybrid DSm model is an important problem in information fusion. But the traditional combination rules are mainly under fixed discernment frame (Shafer model and free DSm model) responding to static model. A new method for dynamic proportional conflict redistribution rules (dynamic PCR rules) based on hybrid DSm model is proposed for the shortness of classical dynamic PCR rules. In the new dynamic PCR rule, combination involved with empty set is defined as one kind to obtain more reasonable results. For the redistribution weight, the conjunction Basic belief assignment (BBA) and conflict redistribution BBA are both taken into account to raise the fusion precision. The effectiveness of revised dynamic PCR rule is studied and simulated in both aspect of fusion accuracy and calculation.
Concerning for air target attribute identification, an attribute identification method based on the combination of multi-class SVM and D-S evidence theory is proposed. The method constructs several multi-class support vector machine (SVM) classifiers, and generates the basic probability assignment (BPA) by the class-wise probability. Then D-S evidence theory is adopted to make the fusion and decision. Simulation results indicate that the method have a good identification of air target attribute, and prove the rationality and validity.
Target attribute identification;Multi-class SVM;BPA;D-S evidence theoryI.
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