## 3 Prediction-Driven Measurement Sub-Partitioning
Moreover, it obtains more accurate estimation performance of the target state and number compared with DP and SP, especially for tracking closely spaced targets. The main reason is that PMS only uses the predicted location of the survival target to obtain the most likely partitions, which facilitates eliminating the inaccurate partitions. More importantly, it fully considers and effectively solves the measurement partitioning problem that the predictive information is not suitable for partitioning the set of measurements in the tracking scenario with targets spatially close. Note that this modification of target predictions does not add extra partitions to achieve better tracking performance. With the limitation of partition number, PMS has a higher tracking speed than DP and SP to achieve accurate estimates of target number and state, which is significant for applications in a real-time target tracking scenario. ## 7 Conclusions

To improve the MCETT performance in the tracking scenario with spatially close targets and to reduce the computational burden, an effective prediction-driven measurement sub-partitioning algorithm (PMS) has been proposed in this paper. By utilizing the target predictions and considering the most likely partitions, PMS can obtain high partitioning accuracy without spending too much time. Moreover, to further improve the robustness of predicted information, the bias predictive information is fully discussed when the number of targets is underestimated in many practical tracking scenarios. More importantly, these target predictions are reasonably replaced by the alternative points

Figure 17: The comparison of four approaches in computation time (seconds). with a correct number to repartition the inaccurate subsets, which is its major advantage over other existing approaches. Due to the concise mathematical forms and favorable measurement partitioning strategies, PMS is simple to implement. This facilitates the derivation of an efficient MCETT approach in the framework of the ET-PHD filter and enables one to estimate the target number and state jointly with promising effectiveness. Furthermore, the cardinality underestimation in closely spaced extended target number may be avoided. Therefore, a large range of the measurement partitioning problems in MCETT can be handled. Simulation results demonstrate that our approach has much less computational time than other existing approaches without degrading tracking performance. In summary, our approach may pave the way for fast and accurate tracking of multiple extended targets in noise and clutter environments. [PERSON] and [PERSON] conceived of the idea and developed the proposed approaches. [PERSON] advised the research and helped edit the paper. [PERSON] and [PERSON] improved the quality of the manuscript and of the completed revision. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Thirteen-Five Equipment Pre-Research Foundation of China (No. 61403120207, 61402100203), the National Natural Science Foundation of China (No. U1504619, 61671139, 61573020), the Aeronautical Science Foundation of China (No. 20185142003), the National Defense Basic Scientific Research Program of China (No. JCKY2018419C001), the Scientific Technology Program of Henan Province, China (No. 182102110397, 192102210064), and the Science and Technology Innovative Teams in University of Henan Province (No. 18 IRTSTHN011). The authors declare no conflicts of interest.