# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 4 Radar Representation
### Automotive Radar  
Automotive radars adopt a strategy of using multiple antennas, either in a single transmitting and multiple receiving (SIMO) configuration or with multiple transmitting and receiving antennas (MIMO) configuration, to determine the DOA. This determination relies on evaluating the phase change between the receiving antennas, coupled with the physical spatial separation between them. The DOA is deduced by performing an angle Fast Fourier Transform (FFT) over the receiver dimension, yielding a 3D tensor named the range-azimuth-Doppler (RAD) map. This tensor can be concatenated with the previously discussed range-Doppler (RD) map, resulting in a comprehensive representation of the direction and Doppler component of each reflection.  
Traditionally, only the azimuth angle has been resolved. However, due to advancements in engineering, modern radar sensors are capable of resolving both azimuth and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Type** & **Data Representation** & **Doppler** & **Senarios** & **Other Sensors** \\ \hline Oxford [63] & SR & RA & \(\times\) & U & CLO \\ \hline Boreas [64] & SR & RA & \(\times\) & S & CLO \\ \hline MulRan [65] & SR & RA & \(\times\) & US & LO \\ \hline RADIATE [66] & SR & RA & \(\times\) & USHP & LO \\ \hline Nuscenes [67] & AR & PC & ✓ & USH & CLO \\ \hline View-of-Delft [68] & AR & 4D PC & ✓ & USH & CLO \\ \hline ColoRadar [69] & AR & ADC,PC & ✓ & ST & CLO \\ \hline USVInland [70] & AR & PC & ✓ & W & CLO \\ \hline Zendar [71] & AR & ADC, RD, PC & ✓ & U & CLOs \\ \hline EU Long-term [72] & AR & PC & ✓ & U & CLO \\ \hline Astyx [73] & AR & 3D PC & ✓ & SH & CLO \\ \hline K-Radar [74] & AR & RAD, 4D PC & ✓ & USHP & CLO \\ \hline RadIAL [75] & AR & ADC & ✓ & USH & CLO \\ \hline \hline \end{tabular}
\end{table}
Table 2: Radar datasets applied for deep learning SLAM methods. Type: SR and AR stand for spinning radar and automotive radar. Scenarios: U,S,H,P,I,T,W stand for urban, suburban, highway, parking lot, indoors, tunnel, and waterways. Other sensors: C,L,O,S stand for camera, LiDAR, internal odometry sensor, and synthetic aperture radar (SAR). ✓ datasets include a Doppler measurement, and \(\times\) do not.  
elevation angles [78; 79]. This enhanced capability gives rise to a 4D radar map, encompassing range, azimuth, elevation, and Doppler features.  
By employing a CFAR detector on the RAD tensor, radar detections can be transformed into point clouds. However, these point clouds often contend with issues arising from road clutter, interference, and multi-path effects due to the noisy IF signal. To refine detections, additional spatial-temporal filtering methods like density-based spatial clustering of applications with noise (DBSCAN) [80] or Kalman filters can be employed.  
Besides the Doppler velocity, another important feature of a radar detection is the Radar Cross Section (RCS), which depends on the properties of the reflected target. It is the intensity recorded by a receiving antenna that was deflected by an object. The intensity is dependent on the material, size, and orientation of the object. The RCS is used to differentiate between different objects. For example, cars and thick walls have a high RCS, whereas pedestrians and small objects have a general low RCS [9].  
Currently, most radar-based SLAM methods lack elevation resolution. Hence, point clouds are commonly represented as 2D BEV images. To enhance object visibility and detection, multiple SIMO/MIMO sensors are often placed on a vehicle to achieve a 360-degree field of vision, often positioned at the corners of the vehicle. However, combining the features of these multiple sensors necessitates further processing due to the spatial and temporal disparities, ultimately resulting in a unified representation. Each detection of the point cloud usually consists of spatial information, Doppler and an RCS component.