## 3 Prediction-Driven Measurement Sub-Partitioning
Compared with SP, we can see that PMS has the better performance in estimation of the target number and state due to their higher precision of the measurements partitioning. This facilitates the accurate

Figure 4: The comparison of four approaches in target number estimation. estimation of the target state and number in the subsequent filtering. It also demonstrates that PMS's strategy of replacing unreasonable target predictions with alternative points is more reliable than the loss function of the K-means++ clustering, which is very likely to inaccurately repartition the subsets. Specifically for PP, its tracking performance is much lower than others. This main reason is that missed detection is likely to bring unreasonable partitioning results of the measurement set, not to mention that only one partition is obtained according to target predictions, which affects the MCETT performance directly. Moreover, PP cannot handle well the tracking of crossed targets, newborn targets, and spawned targets due to the dependence of target predictions. Unlike PP, PMS fully considers the possible changes of target number to narrow the range of partition number and uses the predicted target state to determine the possible clustering centers, which guarantees the accurate partition of the measurement set to improve the performance of multiple extended target tracking greatly. As predictions of the target state and number help PMS obtain the relatively accurate partitioning results, it can be seen that compared with DP, the predictive information is more reliable than the distance information (may contain the distance between clutters with each other) for measurements partitioning when lots of clutters exist in the surveillance area. To sum up, PMS is more reliable than DP, SP, and PP. It has the better tracking performance. Figures 6 and 7 give the partitioning number and computation time of four approaches, respectively. From the comparison results, we can see that the number of measurements partitioning increases as the number of extended targets increases. In other words, the greater the number of measurements partitioning is, the longer the computation time is. Thus, the number of measurements partitioning is the major determinant of the computational burden of different MCETT approaches. Compared with DP and SP, PMS spends less time because it does not need to partition the measurements as much as DP and SP do. This demonstrates that the proposed approach (i.e., PMS) is superior to existing approaches in measurements partitioning for real-time tracking of multiple extended targets. Although PP has the least partition number and computational time, its tracking performance is worse than other algorithms from Figure 5.

Figure 5: The comparison of four approaches in optimal sub-pattern assignment (OSPA) distance. From Figures 4-6, it can be concluded that our approach can handle well the tracking of existing targets, newborn targets, and spawned targets. It not only reduces the computational burden largely for practical tracking applications, but more importantly improves the MCETT performance greatly. Note that this subsection proves that the proposed measurement partitioning algorithm outperforms other existing ones in the typical tracking scenario (including the crossed targets, newborn targets, and spawned targets). For the tracking scenario with closely spaced targets, the tracking performance of the proposed MCETT approach based on PMS needs to be further evaluated in more specific tracking scenarios (i.e., subsections B and C), which is to be demonstrated later. ### Performance Evaluation in Tracking of Extended Targets with Paralleled Tracks

PMS aims to deal with the measurement partitioning problem of closely spaced extended targets. Therefore, in this part, a multiple extended target tracking scenario (i.e., Scenario B) is designed to further evaluate the tracking performance of the proposed MCETT based on PMS.