Calculation and analysis of electromagnetic scattering by helicopter rotating blades

Effective Reconstruction of the Rotation-Induced Micro-Doppler From a Noise-Corrupted Signature

“…We choose the maximum Doppler frequency shift f D,max as the frequency value with an amplitude (energy distributed in the time-frequency plot) of approximately 30% of the maximum amplitude where the distinct contour of the sinusoidal curve induced by the blade tip can be observed. Using the estimated value, the blade length L 2 , which mainly contributes to f D,max , can be estimated as follows [2,[22][23][24]:…”

“…In recent years, timefrequency analysis techniques have been employed to complement these single-domain methods. Previous studies demonstrated that timefrequency analysis techniques clearly represented the time-dependent characteristics of a variety of micro-Doppler phenomena [17][18][19][20][21][22][23][24]. In particular, they could give physical insight into the periodic characteristic of rotation-induced micro-Doppler in the joint timefrequency domain [20][21][22][23][24].…”

Target Classification With Low-Resolution Surveillance Radars Based on Multifractal Features

“…Currently, the research work on the extraction of JEM features is mainly concentrated on estimating the interval of adjacent spectrum lines [8][9][10][11][12][13][14][17][18][19][20][21][22][23][24][25]. However, in the conventional low-resolution radar system, the radar irradiation time towards a target is very short (often 20∼30 ms), and PRF is very low (often a few hundred ∼ a few thousand Hz), so the resolution power in the Doppler domain is lower, and the return signals from different types of aircraft targets are often overlapped in the Doppler domain.…”

“…JEM) features generated by target rotating parts, such as the rotor, empennage, propeller, turbine fan, etc. [8][9][10][11][12][13][14]. JEM modulation features are determined by the leaf number and rotary speed of the rotating parts of a target and independent with the target attitude angle if no LOS-sheltering, i.e., the rotating parts can be seen by the radar.…”

Classification of Aircraft Targets With Surveillance Radars Based on Fuzzy Fractal Features

“…So far, the features extracted in methods with respect to target classification and recognition with low-resolution radars can be divided into three kinds basically: the first kind of features is extracted based on the fluctuation characteristics of return signals from targets, such as the target radar cross-section (RCS), echo amplitude undulation, echo phase undulation, echo vision effect or its 2-D gray-level map [3][4][5][6]; the second kind of features is extracted based on the target motion characteristics, for example, the motion parameters such as the flight height, velocity, acceleration, and time-spectrum (the dynamic trends that target space position as well as its motion state varies with the time is referred to as time-spectrum) [7,8]; the third kind of features is extracted based on the rotational modulation spectra (also called jet engine modulation (JEM) features), which are generated by target rotating parts, such as the rotor, empennage, propeller and turbine fan [9][10][11][12][13][14][15]. JEM features lie on the leaf number and rotary speed of the rotating parts of a target, and are independent with the target attitude if no LOS-sheltering, i.e., the rotating parts can be seen by the radar.…”