## 4 Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\lambda_{c}\)} & \multirow{2}{*}{\(T_{h}\)} & \multicolumn{4}{c}{**WGA**} & \multicolumn{4}{c}{**WAA**} & \multicolumn{4}{c}{**Proposed**} \\ \cline{2-13}  & & **MOOSPA** & **LE** & **ME** & **FE** & **MOOSPA** & **LE** & **ME** & **FE** & **MOOSPA** & **LE** & **ME** & **FE** \\ \hline \multirow{4}{*}{5} & 10 & 117.961 & 0.118 & 0.002 & 2.141 & 117.082 & 0.119 & 0.002 & 2.125 & **29.261** & 0.276 & 0.310 & 0.217 \\  & 50 & 60.364 & 0.117 & 0.004 & 1.091 & 59.793 & 0.118 & 0.005 & 1.080 & **29.522** & 0.218 & 0.360 & 0.173 \\  & 100 & 51.333 & 0.117 & 0.009 & 0.923 & 50.950 & 0.119 & 0.010 & 0.914 & **32.256** & 0.202 & 0.414 & 0.168 \\  & 200 & 36.750 & 0.120 & 0.007 & 0.659 & 35.786 & 0.124 & 0.013 & 0.635 & **29.776** & 0.186 & 0.430 & 0.108 \\  & inf & 70.385 & 0.909 & 1.177 & 0.087 & 53.165 & 0.266 & 0.722 & 0.240 & **29.953** & 0.198 & 0.385 & 0.156 \\ \hline \multirow{4}{*}{10} & 10 & 184.483 & 0.123 & 0.001 & 3.351 & 183.428 & 0.124 & 0.001 & 3.332 & **40.942** & 0.407 & 0.288 & 0.449 \\  & 50 & 113.268 & 0.122 & 0.002 & 2.055 & 112.577 & 0.124 & 0.002 & 2.042 & **41.290** & 0.315 & 0.367 & 0.378 \\  & 100 & 100.047 & 0.123 & 0.003 & 1.814 & 99.686 & 0.125 & 0.003 & 1.807 & **38.121** & 0.303 & 0.387 & 0.301 \\  & 200 & 86.409 & 0.125 & 0.003 & 1.566 & 85.271 & 0.131 & 0.005 & 1.543 & **39.858** & 0.269 & 0.439 & 0.281 \\  & inf & 60.881 & 0.986 & 0.856 & 0.233 & 58.097 & 0.424 & 0.454 & 0.595 & **39.408** & 0.292 & 0.402 & 0.309 \\ \hline \multirow{4}{*}{15} & 10 & 309.273 & 0.129 & 0.002 & 5.619 & 308.569 & 0.129 & 0.002 & 5.606 & **68.585** & 0.638 & 0.185 & 1.051 \\  & 50 & 220.316 & 0.129 & 0.001 & 4.003 & 219.570 & 0.131 & 0.001 & 3.989 & **58.877** & 0.522 & 0.280 & 0.781 \\ \cline{1-1}  & 100 & 204.565 & 0.130 & 0.002 & 3.715 & 204.292 & 0.132 & 0.002 & 3.710 & **58.140** & 0.489 & 0.310 & 0.738 \\ \cline{1-1}  & 200 & 184.782 & 0.136 & 0.002 & 3.355 & 182.942 & 0.144 & 0.002 & 3.321 & **51.550** & 0.455 & 0.342 & 0.587 \\ \cline{1-1}  & inf & 60.596 & 1.075 & 0.424 & 0.659 & 95.898 & 0.539 & 0.155 & 1.579 & **54.910** & 0.526 & 0.287 & 0.702 \\ \hline \hline \end{tabular}  
* The bold values indicate the best MGOSPA result in each row.  
\end{table} 
Table 1: Tracking performance with different clutter numbers \(\lambda_{c}\). Figure 5: Tracking results for \(\lambda_{c}=15\), \(T_{h}=200\). **(a)** Individual sensors’ MGOSPA error without fusion. **(b)** Individual sensors’ CE results without fusion. **(c)** MGOSPA results with fusion. **(d)** CE results with fusion. of WGA, WAA, and the proposed algorithm gradually improved with the increase in the detection probability, which was mainly because the number of false targets decreased with the increase in the detection probability. WGA and WAA still faced the fundamental problem of Bernoulli pairwise threshold selection, which was consistent with the results of the experiments with different clutter numbers. The proposed algorithm was less dependent on this threshold and had a better tracking performance with different probabilities. Simulation 3 was used to verify the effects of different noise intensities on the algorithm's tracking performance. The clutter number \(\lambda_{c}=5\), the detection probability \(P_{d}=0.99\), and the measurement noise intensity \(\sigma_{v}\) were set to 0.1, 0.3, and 0.5, respectively. Table 3 shows the results obtained by averaging the Monte Carlo results over one hundred trials. The tracking performance of the three methods tended to slightly decrease as the noise intensity increased. The proposed method still achieved optimal tracking performance in this experiment.