Since a smaller IDOP corresponds to a higher estimation accuracy, the objective function of the optimal antenna configuration optimization model is shown in a new form, which is as follows:
IDOP provides an effective tool and basis to get the optimal antenna configuration.
The results in Figure 2 are consistent with our analysis, and with the changes of the relative attitude of intersatellite, the value of IDOP corresponds to a consistent change.
An effective antenna configuration strategy is proposed to maintain high estimation precision of the RF relative measurement, which is to select some of the antennas from the reliable positions in real time to form the optimal antenna configuration based on IDOP. According to , antennas should be placed as far apart as possible.
In the use of GA to solve this optimization problem, the purpose is to obtain the optimal antenna configuration corresponding to the smallest value of IDOP. Thence, the IDOP value corresponding to the antenna configuration is considered as the fitness in GA.
In this method, the IDOP values corresponding to the above 10 combinations are traversed and compared, and the smallest IDOP is selected as the individual fitness (28).
Moreover, the IDOP corresponding to the optimal antenna configuration obtained by genetic algorithm is 8.167, which is greatly improved compared with IDOP = 52.28 in the absence of underdoing the proposed GA.
In contrast, in the proposed binary-coded GA, there are 20 iterations, 30 individual fitness is calculated for each iteration, and in each individual fitness calculation, there are [C.sup.2.sub.5]] = 10 times of IDOP calculation which are obtained by the traversal model.