## 3 Prediction-Driven Measurement Sub-Partitioning
This facilitates more accurate partitions of the measurement set and more accurate target estimation. Accordingly, the MCETT performance of PMS will get better and better. Figure 15 also illustrates that PMS is more appropriate to be utilized for tracking of crossed targets. In this case, distance information falls to accurately divide the measurements because of the density distribution of measurements. Though SP works better than DP by using K-means++ clustering, the loss function of K-means++ clustering may lead to the unreasonable subsets, which makes the degradation of target tracking performance. Here, PMS uses the alternative points to replace the unreasonable target predictions and obtain more accurate partitions, which greatly improves the target tracking performance. Figure 14: The comparison of four approaches in target number estimation. Figure 13: True tracks and measurements with clutter. Green square is the beginning point of the track; red circle is the end point of the track. The comparison of partitioning number is shown in Figure 16. The partition number of PMS is much lower than that of DP and SP. From \(k=45\) s to \(k=55\) s, the partition number of DP and SP is a little lower than PMS because measurements are densely distributed making the number of distance thresholds reduced. However, combined with the comparison of tracking performance in Figure 15, it can be seen that the partitions obtained according to these distance thresholds contribute little to the tracking performance. Unlike DP and SP, when targets are crossing, PMS still has almost the same number of measurement partitions as that at previous time steps. Moreover, these partitions obtained by PMS are more accurate than these of DP and SP (as seen in Figure 15). It means that PMS does not add computation time to achieve better tracking performance in this scenario. Because PMS only replaces the unreasonable target predictions in each partition by alternative points to obtain more reliable target centers, which does not increase the extra partitions. The analysis of PP and PMS is detailed in the above subsection. Here, the computation time of four algorithms is given in Figure 17. Combined with Figure 16, we can see that the computation time decreases rapidly when the number of partitions decreases, which implies that the number of partitions can directly affect the tracking speed. In summary, simulation results and performance evaluation demonstrate that our approach outperforms approaches in Scenarios A, B, and C. It can save much computational time because of the limited range of partitioning numbers. Besides, the strategy of using modified predictive information is proved to be more reliable for obtaining better tracking performance. In a word, the tracking

Figure 16: The comparison of four approaches in partitioning number. Figure 15: The comparison of four approaches in OSPA distance. approach based on PMS can successfully realize the adaptive tracking of multiple closely spaced extended targets. ## 6 Discussion

As shown in the above simulation results, the tracking performance of the distance-based measurement partitioning algorithms (e.g., SP and DP) is at the cost of computational burden. Compared with the distance-based measurement partitioning algorithms, the measurement partitioning algorithms based on target predictions have less computational burden for a complete target tracking cycle. This is because only one partition is used for measurement partitioning. However, these algorithms (e.g., PP) may not obtain stable and accurate target tracking performance in the missed detection scenarios due to the dependence of target predictions. Additionally, DP and PP are very likely to underestimate the target number when targets are crossed or parallel. Although SP has a specific strategy for repartitioning the unreasonable subsets, the accuracy of partitioning results needs to be further improved without a loss of computational burden. Unlike PP, PMS deals well with the clutter and missed detection scenarios.