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Applications seeking to exploit electromagnetic scattering characteristics of an imaging or detection problem typically require a large number of electromagnetic simulations in order to understand relevant object phenomena. It has been shown in a previous work that deep learning may be used to increase the efficiency of creating such datasets by providing estimations comparable to simulation results. In this work, we further investigate the utility of deep learning for electromagnetic simulation prediction by adding to the existing training and testing dataset while also incorporating additional material properties. Specifically, we explore using artificial neural networks to learn the connection between a generic object and its resulting bistatic radar cross section, thereby removing the need to repeatedly perform timely simulations. While deep learning can be seen as a computationally expensive technique, this cost is only experienced during the training of the system and not subsequently in the acquisition of results. The goal of this work is to further investigate the applicability of deep learning for electromagnetic simulation prediction as well as its potential limitations. Additionally, performance is compared for different data pre-processing techniques focused on data reduction.

Applications seeking to exploit electromagnetic scattering characteristics of an imaging or detection problem typically require a large number of electromagnetic simulations. Because these simulations are often computationally intensive, valuable resources are required to perform the simulations in an efficient and timely manner, which is not always freely available or accessible. In this work, we investigate the utility of deep learning for electromagnetic simulation prediction. Specifically, we explore using artificial neural networks to learn the connection between a generic object and its resulting bistatic radar cross section, thereby removing the need to repeatedly perform timely simulations. Such a system would be trained in an offline setting and consequently enable rapid bistatic radar cross section predictions for new objects in the future. While deep learning can be seen as a computationally expensive technique, this cost is only experienced during the training of the system and not subsequently in the acquisition of results. The goal of this work is to learn the applicability of deep learning for electromagnetic simulation prediction as well as its potential limitations. Several simple objects are investigated and a thorough statistical analysis will be used to assess the performance of our proposed method.

Synthetic aperture radar (SAR) benefits from persistent imaging capabilities that are not reliant on factors such as weather or time of day. One area that may benefit from readily available imaging capabilities is road damage detection and assessment occurring from disasters such as earthquakes, sinkholes, or mudslides. This work investigates the performance of a pre-screener for an automatic detection system used to identify locations and quantify the severity of road damage present in SAR imagery. The proposed pre-screener is comprised of two components: advanced image processing and classification. Image processing is used to condition the data, removing non-pertinent information from the imagery which helps the classifier achieve better performance. Specifically, we utilize shearlets; these are powerful filters that capture anisotropic features with good localization and high directional sensitivity. Classification is achieved through the use of a convolutional neural network, and performance is reported as classification accuracy. Experiments are conducted on satellite SAR imagery. Specifically, we investigate Sentinel-1 imagery containing both damaged and non-damaged roads.

One promising technique for improving tunnel detection is the use of spotlight synthetic aperture radar (SL-SAR) in conjunction with focusing techniques. Still, clutter arises from surface variations while severe attenuation of the target signal occurs due to the dielectric properties of the soil. To combat these ill-effects, this work aims to improve imaging and detection of underground tunnels by examining the feasibility of matched illumination waveform design for tunnel detection applications. The tunnel impulse response is incorporated in an optimum waveform derivation scheme which aims to maximize the signal-to-interference and noise ratio (SINR) at the receiver output. Numerical electromagnetic simulations are used to consider wave propagation in realistic soil scenarios which include uniform and non-uniform moisture profiles. It is demonstrated that by considering matched illumination waveforms for transmission in SL-SAR systems, an improvement in the detection and imaging capabilities is achieved through enhanced SINR.