## 4 Data Analysis and Classification  
The dataset generated by the Soprano radar system was created by a series of repeat measurements of four separate individuals (labelled A, B, C and D) walking within a closed room in a fixed pattern. The individuals walked up and down within the room directly parallel to the antenna-pointing  
Figure 4: Random Forest Tree example. direction, following the longest wall in the room. The motion was completed with either no objects being carried or with a rucksack bag being held in both hands which weighed approximately 5 kg. This motion was repeated 93 times in total for all individuals. For each repeat measurement a single recording contained multiple passes of the person walking away and then towards the radar system over a 30 s period. An example Range Time Intensity (RTI) plot of a person walking backwards and forwards within the room can be seen within Figure 5; for this example, the person was walking free handed. The range values include the RF cabling lengths, hence, the target appears at a greater distance than the absolute straight-line distance from the antennas to the individual. The RTI shows that the person was detected within the scene, shown by the disturbance in the signatures at 2.5, 12 and 22.5 s close to the wall signature at 7 m. The RTI image is dominated by the very strong component from the wall reflection as well as the further down range sidelobes of this bright target. It is important to remove this component to enhance the dynamic range of the data dedicated only to moving targets within the scene and this was completed using a Moving Target Indicator (MTI) filter, defined in the previous section. A plot of an MTI processed dataset can be seen in Figure 6, using the same data as shown in Figure 5.  
The effect of the MTI filter can be clearly seen in Figure 6, where the moving person target is clearly shown to increase and decrease in range as they walked back and forth within the room. The additional components at longer ranges (greater than 15 m) appear to follow the same slope as the main target and are considered to be generated by multipath due to the complex cluttered scene. The scene used for these experiments was particularly cluttered with lots of opportunity for multipath to be generated. Moreover, the external brick wall which consisted of an air cavity could have potentially contributed a significant amount of multipath, but this represents a real world scenario for the sensor. Figure 5: Range Time Intensity (RTI) plot of individual walking with free hands measured through the wall. The range data was then transformed into the Doppler domain using a Short Time Fourier Transform (STFT) technique. In order to complete this processing, the range bins the target was present within have been selected. A windowed short time FFT processing technique was then applied to these range bins, with a window length of 64 samples and a Hanning weighting. This resulted in a Doppler-time spectrogram that shows both the bulk Doppler component of the person's velocity while walking away and towards the radar sensor, as well as the micro-Doppler components generated from swinging arms, legs, as well as the torso. Examples of micro-Doppler signatures from both RTI and MTI data can be seen in Figures 7 and 8 for free handed walking and walking whilst carrying a bag in both hands, respectively. Figure 6: Moving Target Indicator (MTI) plot of individual walking with free hands measured through the wall. Figure 7: Example Doppler-time spectrogram from a person walking free handed: (**a**) RTI micro-Doppler; (**b**) MTI micro-Doppler. The micro-Doppler data from the MTI signals was then systematically divided into sections of walking towards and walking away from the radar system, which over the full 30 s capture occurred multiple times. The MTI micro-Doppler result was used as it has the dominant zero frequency components removed, which does not contain information useful for motion characterization.