![]() Time-shift invariance features, such as bispectra and higher-order spectra, were applied to target classification. Making use of the invariance property of the Mellin transform, features that are insensitive to the aspect angle of targets were extracted. To overcome these problems, some researchers pay a lot of attention to exploring time-shift invariance features and conduct robust feature templates. Classification performance based on UWB signals suffers from the target aspect, time shift, and amplitude sensitivity. In many applications of machine learning, feature extraction is fundamental for different systems. On the contrary, the time-domain integral equation (TDIE) is more suitable for solving open region problems since the solution of the integral equation automatically satisfies the radiation boundary condition. The finite-differential time-domain (FDTD) method and the finite-element time-domain (FET) method have advantages in processing inhomogeneous materials however, dividing the whole propagation space will generate a large computational domain. Moreover, time domain methods can obtain broadband data through only one calculation and clearly reflect the entire physical process, which differs from frequency domain methods. Considering all EM effects, the full-wave technique is widely used in EM scattering studies for complex targets. High-frequency methods fail to take all EM phenomena into account. Analytical solutions can provide precise results but only for simple regular objects. Some researchers have conducted extensive studies on various EM calculation methods for target scattering characteristics. Generally speaking, there are three major strategies to achieve this goal: practical measurement, scaled experiment, and electromagnetic (EM) simulation however, the first two methods are not only expensive but also struggle to acquire measured non-cooperative signals. One of the critical issues of the target classification is to accurately model the objective and obtain a large amount of echo data. ![]() The results show that the proposed methods achieve promising classification performance under the condition of low signal-to-noise ratio (SNR) values. ![]() ![]() Then, the deep conventional neural network (DCNN) is introduced for the final recognition. Furthermore, given that the actual environment is full of noise and clutter, we propose an improved two-dimensional variational mode decomposition (2D-IVMD), and an algorithm is proposed to eliminate noise and extract edge features preliminarily, which lays a foundation for further in-depth feature extraction. By comparing the simulated waveform with the actual one, the accuracy of the electromagnetic modeling is verified. In this paper, we first utilized 3ds Max to acquire accurate geometric models and applied a time-domain integral equation (TDIE) for echo signal acquisition under the condition that the transmitted signals had an extremely short duration period. Compared with narrow-band and other bandwidth radars, the echo signal of the carrier-free UWB radar includes more comprehensive and detailed information with respect to the targeted object. GOAL STATEMENT : What are your academic goals at City College? Please paste your Goal Statememt assignment into this document here.In recent years, the interest in radar automatic target recognition (RATR) based on the carrier-free ultra-wideband (UWB) radar has been increasing. Use your advising appointment to get answers! If you plan on using coursework from another institution for a VMD degree, you should submit your official transcripts to CCSF before your last semester of study.
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