## 4 Simulation Experiment and Performance Analysis
### Simulation Results of Placement Optimization Based on Genetic Algorithm  
We set the main parameters of the GA algorithm for the scenario in this paper: the population size is taken as 200, the selection parameter is chosen as 0.05 multiplied by the population size, the crossover probability is set as 0.8, and the variogram function parameters Scale and Shrink are both taken as 1 to ensure that global optimization is achieved within the range of values taken. The number of iterations is adjusted according to the number of stations to be deployed, which is obtained by multiplying the number of stations by 600. The accuracy of the variables is chosen as \(1\times 10^{-3}\), and the accuracy of the  
Figure 4: GDOP curves of TOA, TDOA, and TOA/TDOA hybrid positioning of line trajectory. Figure 3: Schematic Diagram of Receiving Station Placement and Target Line Trajectory for TOA/TDOA hybrid source localization system. objective function calculation is chosen as \(1\times 10^{-6}\). The target's line trajectory varies from point (\(5\times 10^{2}\), \(5\times 10^{2}\)) to point (\(5\times 10^{3}\), \(5\times 10^{3}\)) in the YOZ plane, as shown in Figure 3.  
Figure 5 shows the simulation results of the optimal GA algorithm-based station deployment scheme under different numbers of TOA and TDOA stations, where the asterisks in Figure 5a give the simulation results of optimal station placement for five TDOA stations and the asterisks in Figure 5b give the simulated results of optimal station placement for seven TDOA stations. From the figure, it can be seen that when there are only TDOA stations, the optimal station placement is generally symmetric around the target line trajectory, and the station locations are mainly spread out at the four corners of the boundary and below the target trajectory line. When there is a combination of TOA and TDOA stations, the optimization algorithm gives the station placement scheme as shown in Figure 5c,d, where the TDOA stations are mainly located at the four corners of the boundary and the TOA stations are mainly located below the target line trajectory. Based on the optimization results of each of the above placement schemes, we obtained the positioning accuracy or GDOP curve of the target line trajectory from point (\(5\times 10^{2}\), \(5\times 10^{2}\)) to point (\(5\times 10^{3}\), \(5\times 10^{3}\)) in the YOZ plane by simulation, as shown in Figure 6. The line trajectory positioning accuracy curves of the T-shaped placement scheme are also  
Figure 5: Optimal station placement results based on GA algorithm: (**a**) 5-TDOA stations T-type placement and optimized layout structure; (**b**) Optimized layout structure for 7-TDOA stations; (**c**) Optimized layout structure simulation for 1-TOA and 4-TDOA stations; (**d**) Optimized layout structure simulation for 3-TOA and 4-TDOA stations. added in Figure 6, and the T-shaped placement scheme is shown in Figure 5a. It can be seen from the figure that when there are five TDOA stations, except for the positioning accuracy of the optimal deployment scheme and the T-type placement scheme at the height of 500 m-900 m, which is essentially the same, the positioning accuracy of the optimal deployment scheme for other points of the line trajectory is better than that of the T-type deployment station, which indicates that the optimization effect of the GA algorithm on the placement scheme is apparent. Meanwhile, the positioning accuracy improves with the increase in the number of stations, for example, the positioning accuracy of 7 TDOA stations is better than that of 5 TDOA stations, and the positioning accuracy of 3 TOA stations plus 4 TDOA stations is better than that of 1 TOA station plus 4 TDOA stations.