# Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
## 3 Simulation Analysis and Results
### Results of DL
The efficacy of the alternative algorithms was evaluated  
Figure 13: Observer, target’s true and estimated paths using **(a)** UKF only, **(b)** UKF-GRU, **(c)** UKF-LSTM, **(d)** UKF-MLP, and **(e)** UKF-MLP. based on their ability to predict values that closely agreed with the UKF's predictions. Results indicated that both LSTM and GRU models showed high effectiveness in their predictive performance, which can be further explained in Table 5. Based on the available data and information, it can be concluded that using a UKF with the predicted standard deviations from various machine learning models led to accurate prediction of target states. Among the models, CNN showed faster convergence in its predictions, followed by GRU, LSTM, and MLP. Integrating deep learning algorithms with UKF has shown tremendous success in target-tracking applications. In this context, multiple presents the performance of this approach in 5 scenarios. It reports the convergence time for range (R), course (C), speed (S), elevation (E), and pitch (P) for each scenario based on the acceptance criteria specified for all algorithms. Figure 14: Error in estimated (a) speed, (b) course, and (c) pitch. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**SN**} & \multicolumn{4}{c}{**UKF**} & \multicolumn{4}{c}{**MLP**} & \multicolumn{4}{c}{**CNN**} & \multicolumn{4}{c}{**LSTM**} & \multicolumn{4}{c}{**GRU**} \\ \cline{2-19}  & **C** & **S** & **P** & **TC** & **C** & **S** & **P** & **TC** & **Dif** & **C** & **S** & **P** & **TC** & **Dif** & **C** & **S** & **P** & **TC** & **Dif** & **C** & **S** & **P** & **TC** & **Dif** \\ \hline
1 & 109 & 230 & 85 & 230 & 99 & 177 & 46 & 127 & 53 & 113 & 197 & 57 & 197 & 33 & 113 & 185 & 49 & 185 & 45 & 106 & 192 & 57 & 192 & 38 \\ \hline
2 & 30 & 167 & 59 & 167 & 28 & 170 & 72 & 170 & 3 & 29 & 187 & 83 & 187 & 20 & 30 & 166 & 68 & 164 & 1 & 30 & 173 & 69 & 173 & 6 \\ \hline
3 & 40 & 196 & 40 & 196 & 122 & 186 & 20 & 186 & 10 & 123 & 184 & 20 & 184 & 12 & 136 & 199 & 39 & 199 & 3 & 128 & 190 & 20 & 190 & 6 \\ \hline
4 & 116 & 184 & 68 & 184 & 130 & 299 & 40 & 209 & 25 & 129 & 207 & 53 & 207 & 23 & 95 & 186 & 52 & 186 & 2 & 115 & 196 & 49 & 196 & 12 \\ \hline
5 & 145 & 253 & 63 & 253 & 127 & 224 & 51 & 224 & 29 & 127 & 224 & 51 & 224 & 29 & 152 & 262 & 64 & 262 & 9 & 145 & 252 & 62 & 252 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Convergence times. Scenario 2 was used as an example, where the UKF's Total Convergence (TC) was considered ideal since it was simulated using the standard deviation provided in Table 4. By incorporating DL models into the UKF and simulating without assuming the standard deviation, the authors aimed to obtain the same standard deviation, implying that the convergence time should be identical to that obtained when using UKF alone. The results in Table 4 indicate that the LSTM model's predictions were almost similar to the UKF's predictions, with only a one-second difference, followed by the GRU, CNN, and MLP models, respectively. This pattern was consistent across most of the cases examined. Table 3 corroborates these results by showing that the LSTM model had the lowest MSE, followed by the GRU, CNN, and MLP models.