# Decomposition of Multicomponent Micro-Doppler Signals Based on HHT-AMD
## 1 Introduction  
There has been lots of research focusing on the subject of micro-motion characteristics and application of micro-Doppler (m-D) features in radar systems over the past two decades [1; 2; 3; 4]. The micro-Doppler effect was originally proposed by [PERSON] [5] in coherent laser systems, and then introduced to radar applications by [PERSON] [6]. In radar jargon, the micro-Doppler effect is the phenomenon of additional sidebands appearing around the central Doppler spectra of the target, which is highly related to its kinematics and geometric features [7]. Micro-motion and micro-Doppler phenomena can be observed everywhere in our lives and contribute to the fields of radar-based exploration and surveillance of humanity activities [8]. Its underlying civilian and military applications include security monitoring, urban warfare, law enforcement, healthcare, kinematics, search/rescue, anti-terrorism surveillance, and so on [9; 10]. When a radar returned signal from a vibrating/rotating target is viewed in the frequency domain, its micro-Doppler shifts can be seen by their deviation from the center frequency of the radar returns. The Fourier transform is the most common method to analyze the properties of a signal waveform inthe frequency domain. It shows the distribution of the magnitude and the phase at different frequencies contained in the signal during the time interval of analyzing. Although the frequency spectrum may indicate the presence of micro-Doppler shifts, and possibly the relative amount of displacement toward each side, because of the lack of time information, it is not easy to tell the vibration/rotation rate from the frequency spectrum alone. Therefore, the joint time-frequency analysis that provides more insight into localized time-dependent frequency information from the time-varying behavior of the signal is more useful and is complementary to the existing time-domain or frequency-domain methods. The significant characteristic of micro-Doppler signals is the uniqueness because different micro-motions provide discriminative signatures [11]. Micro-Doppler parameters such as Doppler repetition period, Doppler amplitude, and initial phase reflect distinctive features of the target, which are useful for target classification and recognition. The time-varying trajectories of the m-D signals are revealing in the time-frequency (T-F) domain [12]. Conventionally, joint time-frequency analysis could display the instantaneous characteristic and exploit the time-varying signature, thus it has been widely used in micro-Doppler signals feature extraction [13; 14]. However, there are some technical factors to restrain the performance of estimation algorithm for micro-Doppler analysis, e.g., the T-F resolution limitations, multicomponent signals decomposition, and the low ratio of energy in radar echoes [15]. To solve these problems, an approach based on the Hilbert-Huang transform (HHT) method was introduced considering that its distribution does not rely on convolution, avoiding kernel convoluting trouble in Cohen's class T-F representation [16]. After being proposed by Dr. [PERSON] [17] in 1998, the HHT method has been used in various fields related with nonlinear and non-stationary signals such as seismic data analysis, speech analysis, and blood pressure signal analysis because of its high-resolution characteristic. The HHT method is capable of extracting the complicated signal components from the raw data based on empirical mode decomposition (EMD) [18]. The instantaneous frequencies can be extracted from the Hilbert-Huang spectrum ignoring interference of the frequency resolution and time resolution. Since the obtained instantaneous frequencies reveal exactly time-dependent characteristics of the micro-Doppler signatures, HHT was employed for micro-Doppler signals analysis and became an appropriate tool for analyzing m-D signatures [19; 20]. However, considering the previous relative works, some shortcomings of the HHT were also found.