6.1.2. The Number of Targets: 5

In this scenario, we analyzed the clutter measurement density estimation performance by increasing the number of targets to five, as shown in Figure 8. The parameters except the number of targets were the same as in the previous scenario. The number of confirmed false tracks was made almost equal as in the previous scenario by adjusting the initial target existence probability.

Figures 9–11 represent CTTR, position RMSE for Target 1, and the estimated clutter measurement density for Target 1 over time for the scenario, respectively. All the algorithms had the same trend in estimation performance as in the previous scenario. The proposed clutter measurement density estimation method with the sparsity order of *n* = 5 showed the best tracking performance among the methods in comparison because it estimated the clutter measurement density similar to the true clutter measurement density even if the number of closely located targets increased. As shown in Table 2, nCase and nOk for the MTT-SCMDE with *n* = 5 represented the best tracking performance among the adaptive estimation methods in comparison.

**Figure 8.** Simulation scenario with five targets.

**Figure 9.** Confirmed true track rate.

**Figure 11.** True clutter measurement density and estimated clutter measurement density.

**Table 2.** Track retention statistics for Monte Carlo simulation.

