## 3 Smart Collaborative Positioning Using CD Approach
### Best Satellites Selection Algorithm in CD Process  
In the proposed algorithm, the reference station can be fixed or mobile. The idea is to expand the availability of assistance for the mobile user in GNSS-challenging environments. It has been demonstrated in [5] that the mobility of the RS in the CD approach depends mainly on the integration period and the dynamic of the reference station. Thus, in the algorithm of best satellites selection, the receiver that is in good reception condition sends its data (position, pseudorange, ephemerides) to the other receivers in difficulty to allow them to estimate their positions. Weighting costs will then be allocated to all visible satellites according to the quality of the parameters received. This will make the optimal choice of the best receiver among all the reference stations while choosing the receiver that receives the best satellites. The CD algorithm will be able to choose the best satellites intelligently. Finally, to calculate the position, the CD metric will be calculated as a function of time and Doppler. More specifically, the direct positioning algorithm is used to estimate the position according to the best selected satellites, since in the case of a conventional CD, all the available satellites are used. In fact, the algorithm consists of the optimal use of the best satellites to take advantage of the CD's ability to increase sensitivity with fewer satellites and at the same time reduce the complexity of the operation as shown in Figure 4. To implement this idea, the CD algorithm is based on several parameters, the most important of which are the \(C/N_{0}\) level, the  
Figure 3: Influence on CD positioning ambiguity. (**a**) 6 satellites, mask = 10\({}^{\circ}\), GDOP = 1; (**b**) 10 satellites, mask = 10\({}^{\circ}\), GDOP = 1; (**c**) 6 satellites, mask = 10\({}^{\circ}\), GDOP = 2.5; (**d**) 10 satellites, mask = 10\({}^{\circ}\), GDOP = 2.5; (**e**) 6 satellites, mask = 30\({}^{\circ}\), GDOP = 2.5; (**f**) 10 satellites, mask = 30\({}^{\circ}\), GDOP = 2.5; (**g**) 6 satellites, mask = 30\({}^{\circ}\), GDOP = 10; (**h**) 10 satellites, mask = 30\({}^{\circ}\), GDOP = 10.  
elevation angle and the satellites forming the best DOP. The CD acquisition is performed using the BPS (Bi-dimensional Parallel Search) acquisition method to accelerate the computation during the search process in both dimensions (code and Doppler) in parallel. Thus, the new CD metric with the best satellites and reference station is expressed as:  
\[D_{\text{CD}}\big{(}R_{i},\theta_{j},\Delta B_{n},\beta\big{)}=\sum_{k_{bs}}D_{ ind}\Big{(}\hat{\tau}_{k},\hat{f}_{d_{k}}\Big{)} \tag{14}\]  
where \(D_{\text{CD}}\) is calculated by the sum of the correlations of all satellites selected by the algorithm, which are considered the best (satellites with higher \(C/N_{0}\) and with higher elevation angles, and in some cases satellites forming the best GDOP), but not all visible satellites. That is why we have the \(k_{bs}\) index with the sum operator which refers to the best satellites. Briefly, the smart CD algorithm involves an optimization method allowing the use of the best satellites as follows:  
1. For each RS that can assist the user: 1. Get the elevations and azimuths of all satellites in view and sort them with their elevations in ascending order. Figure 4: Flow chart of proposed smart cooperative positioning using CD. * Select the better satellites with higher \(C/N_{0}\): It is necessary to choose a certain value as a threshold for the selection of strong and weak satellites. For example, we can choose the nominal value of 45 dB-Hz as the threshold value. This can be changed according to the objectives set for the receiver design. This operation corresponds to Equation (18). * Select the best satellites with higher elevation angles: It is necessary to choose a certain value as a threshold for the selection of satellites as good or bad according to their angle of elevation.