# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
## 3 Node Weight Allocation Strategy Based on Data Fusion
### Corrected Credibility and Falsity  
In data link networks, when counting the channel traffic pulse, the recorded counting values by node devices can be impacted by various factors such as channel fading, electromagnetic interference, and sensor instability. This can easily result in unreliability in the perception of the channel state. In information theory, entropy is frequently used to describe uncertainty. Consequently, in this paper, we employed entropy to depict the uncertainty associated with the characteristic parameters of node devices. The information entropy is expressed as:  
\[I(\mathfrak{m})=-\sum_{A\in\Theta}m(A)log_{2}m(A) \tag{16}\]  
The greater the information entropy value, the higher the uncertainty of the characteristic parameters. This indicates that the uncertainty of the counting values recorded by the node device is greater, and therefore, the credibility of the node device when used for channel state perception is lower.  
Based on the information entropy of characteristic parameters, a relative certainty index for the characteristic parameters of node devices is calculated to describe the relative degree of certainty. The relative certainty index for the \(j\)-th characteristic parameter is expressed as:  
\[\lambda_{j}=exp\left\{-\frac{I(m_{j})}{\sum_{k=1}^{n}I(m_{k})}\right\} \tag{17}\]  
This expression indicates that a larger information entropy value for a characteristic parameter of a node device corresponds to a smaller relative certainty index, and a greater uncertainty in the counting values recorded by the node device. Conversely, a smaller in-formation entropy value corresponds to a lower uncertainty in the recorded counting values by the node device.  
By introducing the relative certainty index of the characteristic parameters of the node device into the credibility and falsity of the characteristic parameters, and modifying them accordingly, we can further enhance the objective description of credibility and falsity of the characteristic parameters, thereby accurately assessing the traffic pulse statistical capability of the node device. The corrected credibility of the modified \(j\)-th characteristic parameter is expressed as:  
\[Crd^{\prime}(m_{j})=\frac{\lambda_{j}\cdot Crd(m_{j})}{\sum_{k=1}^{n}\lambda_{ k}\cdot Crd(m_{k})}\left\{\sum_{k=1}^{n}Crd(m_{k})\right\} \tag{18}\]  
where \(Crd(m_{j})\) represents the credibility of the \(j\)-th characteristic parameter. \(n\) represents the number of characteristic parameters of the node device. In this paper, \(n=5\), indicating five characteristic parameters, namely receiving sensitivity, antenna VSWR, SNR, the number of operating months, and MTBF.  
The corrected falsity of the \(j\)-th characteristic parameter is expressed as:  
\[F^{\prime}(m_{j})=\frac{\lambda_{j}\cdot F(m_{j})}{\sum_{k=1}^{n}\lambda_{k} \cdot F(m_{k})}\left\{\sum_{k=1}^{n}F(m_{k})\right\} \tag{19}\]  
where \(F(m_{j})\) represents the falsity of the \(j\)-th characteristic parameter and \(n\) represents the number of characteristic parameters.