## 3 Prediction-Driven Measurement Sub-Partitioning
Due to the lower partition number compared with DP and SP, PMS saves a lot of computational time, which is very significant for real-time target tracking. More importantly, from the above total figures, the tracking performance of PMS remains accurate, which is much better than DP, SP, and PP. Figure 4:2 The comparison of four approaches in partitioning number. Figure 4:3 The comparison of four approaches in OSPA distance. To sum up, PMS outperforms other approaches when tracking of extended targets with tracks-paralleled. Its computation burden is greatly reduced without the loss of tracking performance. ### Performance Evaluation in Tracking of Extended Targets with Crossed Tracks

From the above analysis, it can be seen that using alternative points to replace the unreasonable target prediction is useful to obtain relatively accurate measurement partitioning results, which makes PMS effectively solve the cardinality underestimation problem of closely spaced targets. To further evaluate the tracking performance of MCETT approach using PMS, a multiple extended target tracking scenario (i.e., Scenario C) is designed, in which the tracks are crossed. Figure 13 shows the true trajectories of the extended targets with cluttered measurements in Scenario C. All extended targets were born at \(k=1\) s and died at \(k=100\) s. Roughly from \(k=45\) s to \(k=55\) s, all these targets are getting very spatially close and crossing from each other, which is another typical scenario for tracking of multiple extended targets. This scenario, in which the dense distribution of measurements bring huge difficulty for measurement partitioning, is suitable to test the proposed measurement partitioning algorithm, i.e., PMS. The birth intensity is

\[ \begin{array}{l}D_{b}(x)=0.1\mathcal{N}(x;m_{b}^{(1)},P_{b})+0.1\mathcal{N}( x;m_{b}^{(4)},P_{b})+\\ 0.1\mathcal{N}(x;m_{b}^{(3)},P_{b})+0.1\mathcal{N}(x;m_{b}^{(4)},P_{b})+\\ 0.1\mathcal{N}(x;m_{b}^{(5)},P_{b})+0.1\mathcal{N}(x;m_{b}^{(6)},P_{b})\end{array}  \tag{38}\]

where \(m_{b}^{(1)}=[-500,250,0,0]^{\mathrm{T}}\), \(m_{b}^{(2)}=[250,500,0,0]^{\mathrm{T}}\), \(m_{b}^{(3)}=[250,-500,0,0]^{\mathrm{T}}\), \(m_{b}^{(4)}=[-500,-250,0,0]^{\mathrm{T}}\), \(m_{b}^{(5)}=[-50,450,0,0]^{\mathrm{T}}\), \(m_{b}^{(6)}=[450,50,0,0]^{\mathrm{T}}\) and \(P_{b}=\mathrm{diag}\left([100,100,25,25]\right)\). The other simulation parameters are the same as those of the extended targets in Scenario A. In this scenario, PMS is less affected by the underestimated target number when tracking of extended targets with tracks crossed and outperforms other measurement partitioning algorithms (i.e., DP, SP, and PP). The target number estimation of four approaches is shown in Figure 14. It can be seen that PMS has a much better estimation performance of target number than PP and the other two approaches. Especially when targets are crossing, PMS uses the alternative points to replace the unreasonable target predictions, which can greatly avoid inaccurate measurement partitions, i.e., measurements are accurately divided into six subsets rather than one subset. Compared with DP and SP, which wrongly divide the measurements generated from more than one target into one subset when the measurements are densely distributed, this contributes to better performance in estimation of the target number. Figure 12: The comparison of four approaches in computation time (seconds). The OSPA distance of the compared approaches is shown in Figure 15. From Figure 15, we can see that PMS has much better tracking performance than DP, SP, and PP. The main reason for this is that the tracking performance of the PMS is closely related to the target predictions. More importantly, the unreasonable target predictions are replaced by other suitable alternative points, which greatly improve the accuracy of measurement partitioning results. Besides, considering that the target prediction is calculated by the precious target estimation, the more accurate the target estimation is, the more reliable information the target states prediction provides.