High-resolution phase based method for FMCW short range radars

The paper deals with the problem of the using phase-based methods for frequency estimation in the frequency modulated continuum waves (FMCW) short-range radars in the case of high-resolution frequency estimation requirements. The equation is considered for frequency estimation by the least-square method of approximating the phase-to-time relation of the beat signal correlation function in the case of several significant components in the signal (such as valuable and interference signal-like tones). Solution is proposed for the equations by using the parametric (or subspace) decomposition of beat signals (such as eigenvector decomposition, EV). The numerical investigation shows that the bias of the frequency estimations by the proposed solution of the mentioned equation above has statistical properties similar to the method of estimation of signal parameters via rotational invariance techniques (ESPRIT). However, the proposed method does not require the double decompositions, and frequency of each eigenvector can be estimated separately. It is also shown that in the case of the unknown number of signal components the proposed method has the higher statistical properties than for such popular technique as the multiple signal classification method (MUSIC). © 2020 American Institute of Physics Inc.. All rights reserved.The work was supported by Act 211 Government of the Russian Federation, contract N 02.A03.21.0006

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing