# Interpreting radar data#

The data produced by the A121 sparse IQ service is conceptually similar to that of the A111 IQ service. Every sweep is a vector of complex values corresponding to an amplitude and phase of the reflected pulses in the configured range. However, unlike the A111 IQ service, the different points should be regarded as independent as no filter is applied.

For any given frame, we let $$z(s, d)$$ be the complex IQ value (point) for a sweep $$s$$ and a distance point $$d$$.

In the simplest case, we have a static environment in the sensor range. Figure 17 shows an example of this with a single static object at roughly 0.64 m. The sweeps in the frame have similar amplitude and phase, and the variations are due to random errors. As such, they could be coherently averaged together to linearly increase signal-to-noise ratio (SNR). In this context, coherently simply means “in the complex plane”.

In many cases, we want to track and/or detect moving objects in the range. This is demonstrated in Figure 18, where the object has moved during the measurement of the frame. The sweeps still have roughly the same amplitude, but the phase is changing. Due to this, we can no longer coherently average the sweeps together. However, we can still (noncoherently) average the amplitudes.

To track objects over long distances we may track the amplitude peak as it moves, but for accurately measuring finer motions we need to look at the phase. Figure 19 shows the slice of the data along the dashed vertical line in Figure 18. Over the 8 sweeps in the example frame, the phase changed ~ 210°. A full phase rotation of 360° translates to $$\lambda_\text{RF}/2 \approx 2.5 \text{mm}$$, so the 210° corresponds to ~ 1.5 mm.

As evident from the example above, even the smallest movements change the phase and thus move the signal in the complex plane. This is utilized in for example the presence detector, which can detect the presence of humans and animals from their breathing motion. It can also be used to detect a change in signal very close to the sensor, creating a “touchless button”.

As shown, the complex data can be used to track the relative movement of an object. By combining this information with the sweep rate, we can also determine its velocity. In practice, this is commonly done by applying the fast Fourier transform (FFT) to the frame over sweeps, giving a distance-velocity (a.k.a.range-Doppler) map. Figure 20 shows an example of this in which we can see an object at ~ 0.64 m with a radial velocity of ~ 0.5 m/s (moving away from the sensor). This method is commonly used for applications such as micro and macro gesture recognition, velocity measurements, and object tracking.