## 6 Conclusions  
We presented a new idea to reduce the uncertainty at the output of a Kalman filter or of a Kalman filter bank, used for target tracking in automotive RADAR sensor applications, by post-processing with polynomial fitting algorithms. Starting from the conjecture that all the trajectories of terrestrial maneuvering vehicles are polynomial in Cartesian coordinates, we have shown both theoretically and experimentally that a certain localization uncertainty appears at the output of a Kalman filter or of a Kalman filter bank. Based on previous research of the authors about single target tracking [16], and multiple target tracking [6], we have proposed here three polynomial fitting algorithms able to reduce the uncertainty at the output of a Kalman filter or a Kalman filter bank. Inspired by [6], we have highlighted, both theoretically and by simulation, the importance of the cooperation of the Kalman filter algorithm with the Hungarian data association algorithm in the case of multiple targets. We have shown that the uncertainty at the output of a Kalman filter or of a Kalman filter bank can be reduced, eventually losing the chronology, by applying polynomial fitting on both coordinate axes (implicit polynomial fitting). We have proposed three variants of data fitting: implicit [16], explicit and wavelet-based [6] polynomial fitting.  
We have illustrated these methods via five simple numerical examples, realized using synthesized data (the first four experiments, the first two experiments are inspired from [6]) and real data (last experiment concerning a single target with linear trajectory, analyzed in [16] as well). We have shown that the explicit polynomial fitting algorithm is efficient in the case of multiple targets with linear trajectories, that the wavelet-based fitting algorithm is efficient in the case of a single target with non-linear trajectories and that the wavelet-based polynomial fitting and the explicit polynomial fitting outperform the implicit polynomial fitting algorithm. We proved their efficiency by comparison with adaptive Kalman filter solutions, as shown in Table 1.  
Conceptualization, G.M., R.L. and A.I.; methodology, G.M.; software, R.L.; validation, G.M., R.L. and A.I.; formal analysis, A.I.; investigation, G.M.; resources, A.I.; data curation, R.L.; writing--original draft preparation, G.M.; writing--review and editing, G.M.; visualization, R.L.; supervision, A.I.; project administration, G.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable.  
**Acknowledgments:** We acknowledge the valuable comments of the reviewers whose remarks improved the quality of this paper. We have benefited from fruitful discussion with RADAR specialists, such as [PERSON] from Technical University Hamburg Harbor Germany and [PERSON] from Politehnica University Timisoara Romania, who indicated some of the references used in this paper. We also want to thank [PERSON] and [PERSON] from Hella Timisoara for facilitating our access to the automotive RADAR sensor and to the data used in the last experiment reported in this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.