# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
## 3 Node Weight Allocation Strategy Based on Data Fusion
### Credibility of Network Node Data  
Consider the normalized characteristic parameters of the node devices as the evidence parameter set under the recognition framework \(\Theta\)[24; 25] denoted as {\(m_{1}\), \(m_{2}\), \(\ldots\), \(m_{M}\)}. Using an expert scoring method, the trend performance of each parameter in the evidence parameter set is comprehensively evaluated, and a set of scores {\(m_{ij}(H_{1})\), \(m_{ij}(H_{2})\), \(m_{ij}(H_{3})\), \(m_{ij}(H_{4})\), \(m_{ij}(H_{5})\)} is provided, where \(i=1,2,\ldots\); \(n\) represents the \(i\)-th expert; \(j=1,2,\ldots\); \(M\) represents the \(j\)-th evidence parameter; and {\(H_{1}\), \(H_{2}\), \(H_{3}\), \(H_{4}\), \(H_{5}\)} represents the recognition framework \(\Theta\). Each element in the score set corresponds one-to-one with each evaluation index level in the recognition framework \(\Theta\). By averaging the scores of the \(n\) experts, the basic probability assignment function for the \(j\)-th evidence parameter can be obtained, which is expressed as:  
\[m_{j}=\{m_{j}(H_{1}),\,m_{j}(H_{2}),\,m_{j}(H_{3}),\,m_{j}(H_{4}),\,m_{j}(H_{5 })\} \tag{6}\]  
where \(\sum_{k=1}^{5}m_{j}(H_{k})=1\), and \(m_{j}(H_{k})\) represents the probability assignment corresponding to each index level in the recognition framework \(\Theta\).  
Due to factors like the electromagnetic environment, channel interference, and device aging, there are significant differences in the traffic pulse statistical capabilities of node devices within a data link network. These differences are evident in aspects such as the transmission and reception performance, as well as the operational reliability of the node devices. These aspects can be measured using characteristic parameters such as receiving sensitivity, antenna VSWR, SNR, the number of operating months, and MTBF. To more accurately measure this variability, we adopted the Euclidean distance [26; 27] to represent the differences in characteristic parameters among the node devices, which is expressed as:  
\[md_{ij}=2\sqrt{\left|\left(m_{i}-\frac{1}{2}\right)\left(m_{j}-\frac{1}{2} \right)\right|\left(m_{i}-m_{j}\right)^{2}} \tag{7}\]  
The smaller the Euclidean distance between the characteristic parameters of each node device, the greater the similarity between the characteristic parameters, and vice-versa. Based on the Euclidean distance of characteristic parameters, a similarity matrix \(\mathbf{S}\mathbf{M}\) of characteristic parameters is established, expressed as:  
\[\mathbf{S}\mathbf{M}=\left|\begin{array}{cccccc}1&s_{12}&\cdots&s_{1j}&\cdots&s_{1n }\\ s_{21}&1&\cdots&s_{2j}&\cdots&s_{2n}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ s_{i1}&s_{i2}&\cdots&s_{ij}&\cdots&s_{in}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ s_{n1}&s_{n2}&\cdots&s_{nj}&\cdots&1\end{array}\right| \tag{8}\]  
where \(s_{ij}\) represents the similarity among the characteristic parameters of node devices, expressed as:  
\[s_{ij}=1-md_{ij},\,i,j\,=\,1,\,2,\,\ldots,\,\,n \tag{9}\]Based on the similarity matrix _SM_ of characteristic parameters, the credibility of the \(j\)-th characteristic parameter can be calculated and is expressed as:  
\[Crd(m_{j})=\frac{1}{n-1}{\sum_{i=1}^{n}}\begin{array}{c}s_{ij}\\ i\
eq j\end{array} \tag{10}\]  
The credibility vector of the characteristic parameters of network nodes can be acquired, which is expressed as:  
\[Crd(m)=[Crd(m_{1}),Crd(m_{2}),\cdots,Crd(m_{n})] \tag{11}\]  
In this paper, \(n=5\). \(Crd(m_{n})\) represents the credibility of the five characteristic parameters of the node device, namely receiving sensitivity, antenna VSWR, SNR, the number of operating months, and MTBF.