# Tracking Extended Targets: Novel TPMB Filter Driven by Model and Data Collaboration
## 5 Simulation Results and Discussions
### Simulation 2
The experiments reveal that due to the inability of the filters to switch motion models during the target's motion cycle, significant tracking mismatches occur, leading to increased state estimation errors. Since the targets include CV and CT models, which appear with equal probability at certain times, the proposed filter automatically adjusts the gating thresholds based on the target state model, reducing the number of incorrect associations and association duration, thereby enhancing the accuracy of target measurement associations and correcting unstable cardinality estimates. Considering the actual tracking reliability of the filter proposed in this paper under different clutter rates, four types of filters were operated under two different clutter rates. This figure displays a comparison chart of the GOSPA errors of the five filters under these two clutter rates. Notably, as the clutter rate increases, the filter proposed in this paper provides stable cardinality estimates. In situations with noise and unknown covariance, the algorithm proposed demonstrates stable estimation performance. Moreover, in the ASCKF, it also performs well with noise having smaller covariance. This means that the filter proposed in this paper does not exhibit significant changes under various clutter environments. Figure 4: Cardinality statics for four filters under low and high clutter densities. Finally, due to the specificity of the GOSPA evaluation algorithm, it can be differentiated into two parts: the number of missed targets and the number of false targets. The missed and false target state estimates are compared under different clutter rates. Figure 6 shows the missed target state estimation under different clutter rates. It can be seen that all four filters experience a sharp increase in the number of missed targets at the 10 th s, as targets begin to appear, due to the classical filter's lack of a multimodal assumption and the increasing number of targets under the influence of clutter. However, the remaining three filters, having a multimodal assumption, adjust their matching models with changes in the motion state of the models, thus significantly reducing missed targets compared to the classical filter. Compared with TPMB-GBP and TPMB-BP, the improved TPMB-GBP reduced the GOSPA error. Due to the lack of a mixture of adaptive multi-model algorithm for high maneuvering targets, the performance difference between the two algorithms is relatively small. Table 5 shows that TBP-GBP has shorter running time under different clutter and particles. The IMM-TPMB-GBP-CKF, TPMB-GBP, and TPMB-BP algorithms show an increase in missed counts under different clutter levels, but the IMM-TPMB-GBP-CKF algorithm reduces the number of misses in high clutter rates due to adaptive gating adjustments and also reduces operation time. The algorithm proposed in this paper, with the inclusion of ASCKF, reduces target location errors and clutter interference, and the number of missed targets does not significantly change with increasing clutter. Figure 5: GOSPA error for four filters under low and high clutter densities. Figure 6: Number of missed targets for four filters under low and high clutter densities. In order to verify the average running time of the proposed filter in this article under different particle and clutter conditions, we set up a set of comparative experiments to verify its effectiveness. Due to the fact that the GGIW-TPMB filter is a Gaussian implementation method, we only compare the average running time of the other four filters under different particles. Note that TPMB-BP is a particle implementation based on the confidence propagation data association, which results in a large computational workload due to multiple iterations of calculating example weights. On the basis of the same tracking effect, the proposed algorithm reduces the computation of particle likelihood in the target measurement matching process by integrating the TPMB-GBP algorithm for comparison, thereby reducing the overall time.