# An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking
## 5 Simulation Results
### Performance Analysis  
We use two indicators to evaluate the performance of the multi-target-tracking sensor-scheduling algorithms, including tracking accuracy and energy efficiency. In the first set of simulations, we evaluate the tracking accuracy of scheduling algorithms by comparing localization errors and the standard deviation of localization errors. Localization error is the average deviation (in meters) of the estimated location from the exact location of the targets. Based on Equations (20) and (21), average localization error can be calculated by  
\[\overline{error}=\frac{1}{n}\sum_{i=1}^{n}\sqrt{\left(x_{t}^{est}-x_{i}\right)^ {2}+\left(y_{t}^{est}-y_{i}\right)^{2}}, \tag{22}\]  
where \(n\) is the number of targets in the surveillance area. Moreover, we use the standard deviation of localization errors \(\sigma\) for different targets to evaluate the balance of allocation of tracking resources, \(\sigma\) can be calculated by  
\[\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(error_{i}-\overline{error} \right)^{2}}, \tag{23}\]  
where \(error_{i}\) is the localization error of the \(i\)-th target. The smaller the value of \(\sigma\), the smaller the difference in the tracking accuracy for each target, which means that the allocation of tracking tasks by the scheduling algorithm is more balanced. Figure 5 presents the comparison results of the localization errors in the case of 10 targets moving in the surveillance area. MTT-SS achieves lower localization errors than DPT, DIRL, TOTD, and DCBC, which are approximately 48%, 36%, 27%, and 42%, respectively. Additionally, Table 2 gives the standard deviation of localization errors for targets. Combining both results, it can be seen that MTT-SS outperforms DPT, DIRL, TOTD, and DCBC in terms of achieved tracking accuracy. Both DPT and DCBC perform poorly in terms of localization errors and standard deviation of localization errors. The reason is that they do not consider the balance of tracking resources in the scheduling process, which results in unreasonable task allocation. Moreover, DIRL and TOTD solve the multi-target-tracking problem based on the extent RL framework, but no tracking resource balance parameter is considered in the reward function. In contrast, MTT-SS first prioritizes targets with target attributes and network status. Using this information, the sensors can select higher priority targets for tracking under the same conditions. The setting of the reward function makes the sensors consider whether the resources required by the target meet the requirements to select the target to obtain more rewards. This method can balance the allocation of sensor resources among targets, and large tracking errors for individual targets rarely occur. This is why MTT-SS performs better than other methods in terms of tracking errors and the deviation of errors. Figure 5: Localization error. To examine the influence of target number on system performance, we gradually increase the number of targets while other parameters remain unchanged. As shown in Figure 6, the average localization error, which is calculated by averaging the localization errors of all targets for one simulation run, is proportional to the number of targets in the surveillance area. As the number of targets increases, it becomes more difficult for the system to ensure a balanced allocation of sensor resources among targets, which leads to insufficient sensing resources for some targets. This is the reason why the average tracking errors increase with the number of targets. Thus, it is important to design a proper allocation mechanism to allocate sensor resources to different targets. In DPT and DIRL, sensors randomly choose to perform tracking tasks at each time step. Furthermore, sensors in TOTD and DCBC perform a set of tracking tasks according to the distance minimization rule.