# The Impact of Nonlinear Mobility Models on Straight Line Conflict Detection Algorithm for UAVs
## 5 Performance Evaluation
### Slide Behavior with Different Mobility Models
#### 5.2.3 Effect of Mobility Model Parameters  
The parameters of GM give the flexibility to adjust the model to be suitable for different movement patterns. Thus, they need to be chosen based on the physical characteristics of the UAV. In this section, we perform several experiments to evaluate the performance of SLIDE when changing the mobility model parameters. The considered \(T_{b}\) is 2 s, \(T_{l}\) is 8 s, and speed is 3 m/s. First, we test the model with different time step values. Figure 14 shows the number of missed alarms under the following time steps (0.1 s, 0.25 s, 0.5 s, 0.75 s, 1 s, and 1.5 s). The figure shows that when the time step increases, the number of missed alarms decreases. With a larger time step, the drone will move with the same speed and direction for a longer period. In SLIDE, the drones periodically send their current locations and velocities. When a drone receives this information, it calculates if any possible conflict will occur in the near future. This calculation assumes that each drone will continue in the same direction, thus in a straight line. However, using GM, the direction will change randomly at each time step. For example, if the broadcast cycle is 0.5 s and the time step is 0.1 s, each drone will change its heading four times before sending its information again. On the other hand, \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Number of UAVs** & **40** & **60** & **80** & **100** & **120** \\ \hline \multicolumn{6}{c}{With GM} \\ \hline Actual conflicts & 8.40 & 21.47 & 36.40 & 58.47 & 84.00 \\ Number of missed alarms & 1.67 & 3.37 & 5.50 & 9.40 & 14.07 \\ Number of false alarms & 11.77 & 27.87 & 46.20 & 72.63 & 98.10 \\ Missed alarms probability & 0.20 & 0.16 & 0.15 & 0.16 & 0.17 \\ False alarms probability & 1.75 & 1.54 & 1.50 & 1.48 & 1.40 \\ Maneuver time (s) & 2.13 & 2.13 & 2.20 & 2.20 & 2.33 \\ \hline \multicolumn{6}{c}{With EGM} \\ \hline Actual conflicts & 8.73 & 20.40 & 34.47 & 55.20 & 80.87 \\ Number of missed alarms & 1.33 & 3.23 & 4.83 & 9.27 & 13.97 \\ Number of false alarms & 8.13 & 19.63 & 32.30 & 54.37 & 75.73 \\ Missed alarms probability & 0.15 & 0.16 & 0.14 & 0.17 & 0.17 \\ False alarms probability & 1.10 & 1.14 & 1.09 & 1.18 & 1.13 \\ Maneuver time (s) & 2.57 & 2.56 & 2.72 & 2.79 & 2.74 \\ \hline \multicolumn{6}{c}{With RWP} \\ \hline Actual conflicts & 13.07 & 26.33 & 52.13 & 77.73 & 108.53 \\ Number of missed alarms & 0.37 & 0.90 & 1.93 & 3.20 & 3.93 \\ Number of false alarms & 0.83 & 1.97 & 2.60 & 4.50 & 5.83 \\ Missed alarms probability & 0.03 & 0.03 & 0.04 & 0.04 & 0.04 \\ False alarms probability & 0.07 & 0.08 & 0.05 & 0.06 & 0.06 \\ Maneuver time (s) & 3.43 & 3.24 & 3.26 & 3.22 & 3.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: SLIDE scalability. if the broadcast cycle is 0.5 s and the time step is 1.5 s, the drones will change their heading once after sending their information so that the location will be accurate for around 1 s.  
To understand how changing the standard deviation affects SLIDE's performance, we vary the standard deviation of the angle, pitch, and speed in Figure 15a-c, respectively. For instance, Figure 15a depicts the result of changing the standard deviation of the angle from 0.5 to 20. The average number of missed alarms remains constant for the standard deviation from 0.5 to 7.5, then increases incrementally. Changing the angle's standard deviation controls the direction change in each step, then the node will move in the new direction until the next time step. We can also see a similar effect from changing the pitch standard deviation in Figure 15b. We vary the standard deviation of the pitch from 0.1 to 20. The average number of missed alarms remains constant for the standard deviation from 0.1 to 7.5, then increases incrementally. Changing the standard deviation of the pitch controls how high (or low) the drone can move far from the initial altitude. With higher values for the pitch standard deviation, the drone altitude difference is more.