Synthetic aperture radar (SAR) automatic target recognition (ATR) has been an interesting topic of research for decades. Existing methods perform the ATR task after image formation. However, in principle, image formation does not provide any new information regarding the classification task and it may even cause some information loss. Motivated by this, in this paper, we examine two SAR ATR frameworks that work in the phase history domain. In the first framework, we feed the complex-valued phase histories to a deep convolutional neural network (CNN) directly, and in the second one, we perform image formation, phase removal, and phase history generation before feeding the data to the CNN. CNNs are known for their superior performance on image classification tasks. The effectiveness of CNNs is based on dependency patterns in a given input. Thus, the input of CNNs is not limited to images but any input exhibiting such dependencies. Since complex-valued phase histories also have such a structure, they can be the input of a CNN. We perform ATR experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database and compare the results of image-based and phase history-based classification.
This paper presents detailed study on the development of a fiber optic sensor system to design a pressure sensor with different sensor configurations. The sensor used in the experiments is based on modal power distribution (MPD) technique. MPD technique is spatial modulation of the modal power in multimode fibers. Stress measurements and CCD camera based techniques were investigated in this research. Differently from earlier MPD works, all of the data gathered from CCD camera are used instead of using some part of the data, the ring shaped pictures taken from the CCD camera converted to polar coordinates, and so stripe shaped pictures are obtained. Four different features are calculated from these converted pictures. R component of the center of mass in the polar form is the first feature. It is calculated because it was expected to decrease monotonically with respect to increasing applied pressure. Second and third features are ring thickness in polar form with taking brightness of each pixel into account and ring thickness in polar form without taking brightness of each pixel into account. These features are calculated to analyze the effect of each pixel’s brightness. It was expected for these two features that there will not be a big margin between them. Fourth feature is the ratio between third feature and first feature. A MATLAB code is written to correlate these features and applied force to the sensor. Various experiments conducted to analyze this correlation. Pictures are taken from CCD camera with 1 kg steps and from the written MATLAB code, graphics of each feature versus the applied force are generated. Experimental results showed that, the sensitivity of the proposed sensor is much higher than sensors that uses only some part of the collected data in earlier MPD studies. Furthermore, results are almost exactly the same that what was expected for the four proposed features. Results also showed that converting pictures to the polar form increases the sensitivity and reliability.