# mmHSE: A Two-Stage Framework for Human Skeleton Estimation Using mmWave FMCW Radar Signals
## 3 Radar Data Acquisition and Processing
### Signal Processing Pipeline for RA Map Generation  
The conversion of raw IF signals into RA maps involves a structured signal processing pipeline designed to extract spatial and motion-related features of targets. The resulting RA map presents target reflection intensities over a two-dimensional space, with range \(R\) along the horizontal axis and azimuth angle \(\theta\) along the vertical axis, and serves as a crucial input for subsequent detection and pose estimation tasks. As shown in Figure 4, the key stages of this pipeline are described as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Parameter** & **IWR1642 BOOST** & **IWR6843 ISK** \\ \hline \(f_{0}\) & Starting Frequency & 77 GHz & 60 GHz \\ \(BW\) & Effective Bandwidth & 3.001 GHz & 3.001 GHz \\ \(N_{TX}\) & Number of TX Antennas & 2 & 3 \\ \(N_{RX}\) & Number of RX Antennas & 4 & 4 \\ \(N_{s}\) & Number of Samples per Chirp & 256 & 256 \\ \(N_{chirp}\) & Number of Chirps per Frame & 32 & 32 \\ \(T_{m}\) & Frame Period & 50 ms & 50 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dual millimeter-Wave radar configuration parameters.  
Figure 3: The data collection and annotation process utilizes the following hardware: (**a**) the IWR1642 BOOST radar for data acquisition, (**b**) the IWR6843 ISK radar for data acquisition, (**c**) the Kinect V2 camera for data annotation, and (**d**) the annotation of bone points output by Kinect V2.  
Range Processing: A one-dimensional Fast Fourier Transform (FFT) is performed along the fast-time axis (i.e., within each chirp) to obtain range resolution. For the \(i\)-th chirp, \(m\)-th antenna, and \(n\)-th ADC sample, the output is \[S_{R}(i,k,m)=\sum_{n=0}^{N_{\rm{R}}-1}s_{\rm{IF}}(i,m,n)\cdot e^{-j\frac{2\pi i }{N_{\rm{S}}}},\] (7) where \(s_{\rm{IF}}(i,m,n)\) is the sampled IF signal, \(N_{\rm{S}}\) is the number of ADC samples per chirp, and \(k\) denotes the range bin index. * Doppler Processing: A second FFT is applied along the slow-time axis (i.e., across multiple chirps) to resolve the Doppler shift associated with target motion \[S_{RD}(p,k,m)=\sum_{i=0}^{N_{\rm{R}}-1}S_{R}(i,k,m)\cdot e^{-j\frac{2\pi i}{N_{ \rm{C}}}},\] (8) where \(N_{\rm{C}}\) is the number of chirps per frame, and \(p\) indexes the Doppler bins. * Angle Processing: To determine the azimuthal direction of arrival, an FFT is performed across antenna elements \[S_{RDA}(p,k,q)=\sum_{m=0}^{N_{\rm{RX}}-1}S_{RD}(p,k,m)\cdot e^{-j\frac{2\pi m }{N_{\rm{RX}}}},\] (9) where \(N_{\rm{RX}}\) is the number of receiver antennas, and \(q\) denotes the angle bin index. * RA Heatmap Generation: Finally, the RA heatmap \(S_{RA}(k,q)\) is obtained by either extracting the zero-Doppler slice (to suppress moving targets) or integrating over all Doppler bins to preserve moving target energy \[S_{RA}(k,q)=S_{RDA}(0,k,q)\quad\text{or}\quad S_{RA}(k,q)=\sum_{p=0}^{N_{\rm{ C}}-1}|S_{RDA}(p,k,q)|^{2}.\] (10)  
RA maps, as high-resolution spatial representations of target reflection signals, provide critical inputs for subsequent tasks such as target detection, classification, and pose estimation. In this study, we use a standardized FMCW radar signal processing pipeline to generate RA maps. Although existing studies, such as [29; 30], have proposed enhancement methods based on super-resolution, we chose a standardized processing approach to ensure the reproducibility of the system. Future work can explore the impact of high-resolution representations on performance.