4.1.4. Discussion on the Simulation Results

For all simulated experiments, we observe lower OSPA and OSPA<sup>2</sup> errors for the proposed tracker compared to the GLMB filter results. In the first two experiments with the standard observation model, as the clutter rate is high, the filtered-only trajectories jiggle around the true paths due to false measurements. In Figures 2 and 6 as well as in Figures 3 and 7, the overall errors of the GLMB filter estimates are higher than of the proposed tracker estimates. The reduction of localization error contribute mainly to the improvement of the tracking performance. From the cardinality plots in Figures 4 and 8, on average, the proposed tracker slightly improves estimate cardinality performance as it is able to eliminate track fragmentation while eliminating incorrect tracks at some time steps.

In the hybrid TBD tracking experiment, as tracks are miss-detected due to low SNR, the proposed tracker improves tracking performance by eliminating track fragmentation. Not much localization error is reduced by the smoother step as the GLMB filter produces relatively good tracking results. The OSPA and OSPA2 results presented in Figures 11 and 12 show slight improvement of the proposed tracker results compared to GLMB filter tracking results. However, the cardinality plot in Figure 13 clearly indicates that the proposed tracker is able to improve the estimated cardinality between time step 30 and 40.

The run time for all simulated scenario is given in Figure 14 in terms of the percentage of extra computational time of the proposed tracker over the computational time of the filtering step only. It is shown that the extra computational time is negligible in all three tracking scenarios with the extra computational time of the proposed tracker less than 0.5% of the filtering computational time. However, the main disadvantage is that the tracker needs to wait until the end of the smoothing interval to be able to produce tracking results.
