## 4 Simulation Results
### Scenario-Ct  
Similarly, we also analyze detection and tracking performance under the CT model. The average of the detection probability of detection with different SNRs of the three algorithms for 100 Monte Carlo simulations (MC) is compared in Table 3. Figure 5 shows the results in terms of the probability of existence in different frame times for 100 MC.  
The target exists if the probability of existence is higher than 0.7, which is plotted as a horizontal line in Figure 5. It can be seen from Figure 5 that, for SNR =1, 3, 6, 9 dB, the three algorithms also have target presence and absence declaration delays, and each algorithm follows the true existence of the target much more closely for the higher SNR cases, with performance degrading as SNR reduces under the CT model. For the CT model, the HDE PF-TBD algorithm provides higher target detection confidence than the other two algorithms. Moreover, the HDE PF-TBD algorithm has less target presence delays than the others. Meanwhile, we also can see that the probability of existence of the proposed algorithm has a large increase when the SNR is 3 dB and 1 dB. For clarity, the data statistics of Table 3 have proven that the HDE PF-TBD has better detection sensitivity when the SNR is lower.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm** & **9 dB** & **6 dB** & **3 dB** & **1 dB** \\ \hline MultinomialR PF-TBD & 0.747 & 0.621 & 0.378 & 0.275 \\ SystematicR PF-TBD & 0.768 & 0.648 & 0.399 & 0.284 \\ HDE PF-TBD & 0.827 & 0.743 & 0.501 & 0.367 \\ \hline \end{tabular}
\end{table}
Table 3: The average detection probability for the CT model.  
Figure 5: _Cont._  
The results in terms of mean RMSE for the different SNR values of the three algorithms for the CT model are compared in Table 4. Figure 6 shows the results in terms of RMSE in position with corresponding frames for different SNR values for the CT model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **9 dB** & **6 dB** & **3 dB** & **1 dB** \\ \hline MultinomialR PF-TBD & 1.910 & 2.1556 & 2.7044 & 2.909 \\ SystematicR PF-TBD & 1.745 & 1.8739 & 2.6258 & 2.81 \\ HDE PF-TBD & 1.433 & 1.4477 & 2.3836 & 2.3541 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The average RMSE at different SNRs for the CT model.  
Figure 5: **(a)** Probability of existence at SNR = 9 dB for the coordinate turn (CT) model; **(b)** probability of existence at SNR = 6 dB for the CT model; **(c)** probability of existence at SNR = 3 dB for the CT model; **(d)** probability of existence at SNR = 1 dB for the CT model.  
Figure 6: _Cont._  
For the CT model, it can be clearly seen from Table 4 and Figure 6 that the proposed algorithm also shows a better performance on the low SNR target than the other two algorithms. Although the RMSE of the proposed method is a little higher than the other two algorithms at Frame 10 when SNR is 9 dB, the tracking accuracy of the HDE PF-TBD algorithm is still higher than other two algorithms in the whole frame range that the target exists. Therefore, as can be seen from the two aspects of detection and tracking, the proposed algorithm is also capable of tracking the target with the CT motion model and low SNR, and it outperforms SystematicR PF-TBD and MultinomialR PF-TBD.