## 5 Discussion
We present some results in Table 1.  
Let us consider now the solution based on post-processing the results of the Kalman filter. In this paper, we realize a treatment equivalent with the automatic tuning of the Kalman's filter hyper-parameters by the cooperation of the Hungarian data association algorithm with the Kalman filter. The Kalman filter is evaluated by the Hungarian algorithm. We do not need to use an adaptive Kalman filter to track the targets sensed by an automotive RADAR sensor, because the Hungarian data association algorithm \"adapts\" the data from the input of the Kalman filter. Hence, the cooperation between the Hungarian data association algorithm and the Kalman filter is equivalent with the building of an adaptive Kalman filter. The central part of our proposal consists of a post-processing step, based on polynomial fitting. This idea was exploited first in [16]. The algorithms in [2; 10; 11; 12; 13; 14] can substantially reduce the uncertainty at the output of the Kalman filter but not completely. Contrary to the algorithms in [2; 10; 11; 12; 13; 14], the explicit polynomial fitting algorithm and the wavelet-based polynomial fitting algorithm proposed \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **[2]** & **[10]** & **[11]** & **[14]** & **Proposed** \\ \hline \multirow{3}{*}{Accuracy} & Prediction Error Error & RMS Error & Maximum Error \(\leq\)5 m & Positional standard \(\leq\)5 m & Estimation Error \(\quad\) 0 m \\  & 2.97 m & & & & \\ \hline \multirow{2}{*}{Run time} & \multirow{2}{*}{135 s} & \multirow{2}{*}{20–30 min.} & \multirow{2}{*}{Not available} & Iterative method & \multirow{2}{*}{30 s} \\  & & & & & \\ \end{tabular}
\end{table} 
Table 1: Performance comparison of different methods. in this paper are able to completely reduce the uncertainty at the output of the Kalman filter, as is shown in the experiments reported in the previous section and is resumed in Table 1. So, the accuracy of targets' localization obtained by applying the proposed algorithms is higher than in the case of the algorithms proposed in [2, 10, 11, 12, 13, 14]. The algorithms in [2, 10, 11, 12, 13, 14] are used in different applications of the Kalman filter, comprising GPS, unmanned aerial vehicles (UAV), robots, automotive, navigation and tracking, implying some very complicated time trajectories. We concentrate in this paper only on automotive tracking. This choice explains the simple scenarios used in this paper, the targets' trajectories in the case of automotive RADAR being constrained by the physical characteristics of roads. By comparing the run time of the algorithms reported in [2, 10, 14] with the run time of the polynomial fitting algorithms proposed in this paper (see Table 1), we remark that our algorithms are faster. As future work, we intend to apply the proposed algorithms in more complex tracking scenarios. If it will work, we will try to implement the post-processing algorithms into a digital signal processor. One of the possible applications of target tracking is the classification of targets. Tacking as model the video tracking, we will try in the future to apply the classification method described in [26] for RADAR targets based on the proposed enhanced tracking algorithms.