## 4 Simulation and Discussion
### Simulation Results Comparison and Discussion
Using MAM algorithm has reduced the average \(V\) RMSE by 9.36% than using three-model IMM algorithm for tracking Scenario 1, by 9.52% than using AGIMM algorithm for tracking Scenario 1, by 8.45% than using HGMM algorithm for tracking Scenario 1, by 12.18% than using three-model IMM algorithm for tracking Scenario 2, by 12.20% than using AGIMM algorithm for tracking Scenario 2, by 9.50% than using HGMM algorithm for tracking Scenario 2, respectively. Using MAM algorithm has reduced the average \(r\) RMSE by a certain degree than using other algorithms. The results that MAM (single model) algorithm outperforms other algorithms mentioned in this paper for tracking HGV trajectories contradicts the usual opinion that multiple model tracking algorithm outperforms single model (MAM) tracking algorithm for this so-called strong maneuvering target. The HGV can make maneuver at big degree in long flight distance and maneuver strongly in three dimension from the aspect of kinematic models as Figure 1. However, the maneuver ability is not that strong as we think from the aspect of aerodynamic model because the maneuver is caused by the two control variables and the rates of change of the two control variables are not large. First, by comparing nine-model IMM algorithm with three-model IMM algorithm, we know the modeling method by Equation (20) is better cover the variation ranges of bank angle than multiple model method. Second, by comparing MAM algorithm with three-model multiple model algorithms in this paper, we know the modeling method by the equation for attack angle in Equation (19) is better to cover the variation ranges of the attack angle than the multiple model methods. Third, by comparing MAM algorithm with nine-model IMM algorithm, we known the modeling method by Equation (19) is better to cover the variation ranges of bank angle and attack angle than multiple model method. Moreover, we know from Figures 2 and 3 MAM algorithm has a good attack angle and bank angle tracking performance for tracking Scenario 1 and Scenario 2. However, when tracking Scenario 2 the bank angle RMSE by nine-model IMM algorithm is lower than that by MAM algorithm. The reason is that the lateral maneuver is the position change and the position change is lagging behind acceleration change. Based on above discussion, we can know the modeling method of rates of change of the two control variables in the modified aerodynamic model for HGV tracking is better than multiple model method. The VSMM algorithms are more efficient than FSMM algorithms as we know. In this paper, the AGIMM algorithm and HGMM algorithm perform better than IMM algorithm for HGV tracking. Furthermore, HGMM algorithm outperforms the AGIMM algorithm but has almost double computational burden. That is because that HGMM algorithm has a fixed coarse grid which covers the all possible and can overcome the mode hoping. There is only one model filter in MAM algorithm. There are three model filters in both three-model IMM algorithm and AGIMM algorithm. There are nine model filters in nine-model IMM algorithm and six model filters in HGMM algorithm. The numbers of model filters determine mainly the computational cost of HGV tracking algorithm. Obviously, the MAM (single model) algorithm has the least computational cost. Furthermore, the MAM algorithm has the best average tracking accuracy in all these representative algorithms. Based on the above consideration, MAM algorithm is the most cost-effective algorithm for HGV tracking in those algorithms, with better accuracy and much less computational cost than other algorithms in this paper. By comparing Table 1 with Table 2, we can know the \(V\) peak RMSE and \(r\) peak RMSE of Scenario 1 are higher than that of Scenario 2. This is because Scenario 1 maneuvers more strongly than Scenario 2 in peak points.