# Experimental Evaluation of a MIMO Radar Performance for ADAS Application
## 2 Materials and Methods  
In each scenario, the targets that carry a certain speed (radial with respect to the radar) have been analyzed separately to improve target separation and minimize background noise. That is, the speed estimation, obtained thanks to the Doppler shift of the signals, is carried out with the sole purpose of selecting particular targets, pedestrians [19; 20] or motorcycles in our case. This way of proceeding is the natural one after performing a two-dimensional FFT of the received signals. It is the standard method of analysis, as shown in the bibliography provided by TI and in other references [21; 22; 23; 24]. The emitted signal is made up of FMCW signals, where the frequency increases linearly over time in each chirp. The slope selected for the temporal frequency variation  
Figure 1: Main lane of the parking lot where the measurements reported in this work have been carried out. Photograph was taken from the same point and with the same orientation as where the radar was located. was \(S=20\) MHz/us. The sampling frequency in all cases has been \(f_{s}=12\) MHz and a number of samples were taken per chirp \(N_{sc}=256\). Signals are processed in blocks, called frames, with \(N_{cf}=128\) or \(N_{cf}=256\) chirps per frame in this work. The chirps are separated by a certain interval, which can be modified [25]. When the \(N_{sc}\) samples of the \(N_{cf}\) chirps are received, that is, a frame, the samples are ordered by rows in a two-dimensional matrix \(N_{sc}\times N_{cf}\). A 2D-FFT is performed, and the range-doppler map (or range-velocity map) is obtained. From it, you can select the columns of interest (targets with a certain speed) and the different targets present with that velocity. In our case, we have analyzed the targets detected at all present speeds except zero speed; that is, static targets with respect to the radar are ignored. This was performed because the measurements were carried out in a parking lot with parked vehicles, which were of no interest to the study. The selection of received echoes, both in speed and distance, has been carried out by applying a CA-CFAR (cell averaging constant false alarm rate) algorithm [26; 27; 28; 29; 30]. The CFAR algorithm creates an adaptive threshold to discriminate the peaks in the FFT function in order to select the more likely echoes in the scene. In the selection of speeds, an undemanding value has been set, with a probability of false alarm \(PFA_{vel}=0.2\), or 0.3 in order not to lose echoes, while in distance, the results obtained for different speed values have been evaluated, with \(PFA_{range}\ \in\ (0.1\)-\(0.001)\). A small value of PFA produces a more selective threshold. The parameters of interest were the number of false targets in each scenario, which gives us a measurement of the final effective false alarm probability (\(PFA_{eff}\)), the error in estimating the distance of the targets, the maximum errors in distance, and the number of useful echoes obtained per second. The average error in distance, as well as the maximum errors, have been evaluated by measuring the error in each echo considered good by the algorithm. To do this, it is not enough to evaluate the error of the echoes with respect to the final trajectory traced by the target; it is necessary to track the position of each echo and compare it with the estimated instantaneous position of the object. In our case, we did not have a more accurate system capable of providing the instantaneous trajectory of the targets, so an estimate of the likely instantaneous trajectories has been built from the radar echoes themselves. The moment in which the echo appearing on the screen is assigned to a particular target is sometimes critical because, in reality, you cannot be certain that this echo really comes from that target. This fact adds a point of uncertainty to the process, which, in any case, is inevitable. The recorded videos of the different scenes are used to visually validate the movement of the targets and estimate the temporal duration of the measurements.