## 1 Introduction
However, it may not handle well the tracking of extended targets with different sizes. To deal with this problem, the EM partition algorithm was proposed to obtain the better measurement partitions in which the Gaussian mixtures are initialized with the predicted GIW components. Due to the utilization of predictive information, these two algorithms can improve the tracking performance and save computational time. Although the predictive information is promising to be utilized for achieving better performance, its accuracy is closely related to the predicted target number and state. Especially for the tracking of multiple closely spaced extended targets, obtaining accurate partitioning results is difficult when the target number is underestimated. Moreover, it is very likely to gain non-information subsets in missed detection environment, which directly leads to the instable estimate performance of target number and state. To deal with this problem, the distance partitioning algorithm was extended with sub-partitioning and proposed in [27]. It uses the K-means++ clustering [38; 39; 40] to repartition the inaccurate subsets. Unfortunately, it may not possibly guarantee the accuracy of repartitioning results. The main reason lies in the loss function of K-means++, which is likely to cause an unreasonable repartition of subsets and degrades the tracking performance. Overall, the main difficulties of measurement partitioning for MCETT are summarized as follows. (i) How does one maintain the accuracy of the indispensable measurement partitioning results in clutters and missed detection environment without sacrificing the tracking performance? (ii) How does one achieve high tracking performances in MCETT approaches, i.e., obtain relatively accurate partitions when tracking closely spaced extended targets in practical scenarios (e.g., tracks crossed or paralleled)?This paper is motivated by these problems and proposes a prediction-driven measurement sub-partitioning (PMS) algorithm for tracking multiple closely spaced extended targets. The predictive information is utilized to determine possible clustering centers. Moreover, unreasonable target predictions (whose corresponding subset may contain measurements from more than one target when the target number is underestimated) are replaced by alternative points to obtain relatively accurate partition results when targets are spatially close. This proposed partitioning strategy fully considers the spatial distribution characteristics for the clustering center and target predictions, which help obtain the clustering centers close to the true target centers. Thus, PMS handles well the tracking of closely spaced extended targets and seems promising. To reduce the computational complexity, the range of partition numbers is narrowed to eliminate unreasonable and redundant partitions. Note that the proposed algorithm considers all possible changes in the number of targets at each time step and reduces the number of partitions as reasonably as possible. Due to its concise mathematical forms and favorable properties (modifying target predictions and reducing partition number), an approach to multiple extended target tracking is proposed. Compared with other existing approaches based on different measurement partitioning algorithms, the innovative aspects of this work are summarized as follows. (i) In PMS, target predictions are explicitly considered and fully utilized to improve the accuracy of measurement partitioning, which facilitate the performance improvement of multiple closely spaced extended targets tracking. (ii) Due to the concise mathematical forms and favorable properties of PMS, redundant measurement partitions can be eliminated so that the computational burden is largely reduced. (iii) When extended targets are spatially close, PMS seems more promising because the unreasonable target predictions can be marked and replaced by alternative points. It solves the so-called cardinality underestimation problem without adding extra measurement partitions.