New structure for multi-aspect SAR image target recognition with multi-level joint consideration

Abstract

A new structure for SAR target recognition using multi-aspect SAR images is proposed. The new structure applies new approaches in the two levels of SAR image target recognition, which are data level and decision level, and combines the benefits of the two. Projections onto convex sets (POCS) super-resolution reconstruction algorithm is used in the data level of the new structure, which is to advance the resolution of the SAR image by using a series of multi-aspect SAR images. Weighted Bayes decision fusion algorithm is proposed in the decision level to jointly consider the benefit from the data level. All outcomes from the classifiers are fused in the decision level to generate the final result, which combines the multilevel benefits. Verification and analysis is performed to the proposed structure with multitarget image data in MSTAR database. Experimental results indicate that using the proposed structure for multi-aspect SAR images with multi-level joint consideration, the recognition rate is significantly advanced than that using single SAR target image. Meanwhile, the recognition rate by this structure is also higher than that using individual level approach.

On differential depletion length of Schottky diodes with copper-nickel alloy metal of front contact

“…According to thermionic emission theory, the Schottky diode's current-voltage characteristics are already expressed in the literature [10][11][12][13][14]. One can find the I-V graph for different metal types in [10][11][12][13][14]. In the case of forward bias current for nonideal conditions, the equation is expressed as follows:…”