Abstract When starting any GNSS measurements, there is a need to establish a survey plan with the required optimal baselines. The optimal GNSS baselines can be chosen by solving the geodetic second-order design (SOD). The particle swarm optimization PSO is used widely to solve geodetic design issues. This work employed the particle swarm optimization (PSO) algorithm, a stochastic global optimization method, to select the optimal GNSS baselines. The optimal baselines satisfy the set criterion matrix at a reasonable cost. The fundamentals of the algorithm are presented. The effectiveness and usefulness of the technique are then demonstrated using a Nile Delta GNSS network as an example. In some cases, we have to observe many GNSS benchmarks with limited instrumentations. PSO represents a powerful tool for optimizing baseline to get the required accuracy with limited capabilities (like limited receivers). The PSO algorithm, a stochastic global optimization approach, was used in this paper to find the best observation weights to measure in the field that will match the predetermined criterion matrix with a fair degree of precision. The method’s fundamentals are presented with an actual geodetic network over the Nile delta in Egypt. In the current work, two survey strategies were applied. One represents a case with 9 GNSS receivers (high capability), and another one represents the tested survey plan with limited GNSS receivers (3 receivers, low capability) after applying PSO. By comparing two survey strategies, applying the PSO algorithm to a real Nile delta geodetic network shows its effectiveness on the obtained coordinate accuracy. This obtained accuracy ranged from 2?mm to 3?mm in X, Y, Z, and 3?mm in height. Also, the linear closure error between known and estimated coordinates improved to be 1.4?cm after applying PSO.
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