An apparent delay is occurred in GPS signal due to both refraction and diffraction caused by the atmosphere. The second region of the atmosphere is the ionosphere. The ionosphere is significantly related to GPS and the refraction it causes in GPS signal is considered one of the main source of errors which must be eliminated to determine accurate positions. GPS receiver networks have been used for monitoring the ionosphere for a long time. The ionospheric delay is the most predominant of all the error sources. This delay is a function of the total electron content (TEC). Because of the dispersive nature of the ionosphere, one can estimate the ionospheric delay using the dual frequency GPS. In the current research our primary goal is applying Precise Point Positioning (PPP) observation for accurate ionosphere error modeling, by estimating Ionosphere delay using carrier phase observations from dual frequency GPS receiver. The proposed algorithm was written using MATLAB and was named VIDE program. The proposed Algorithm depends on the geometry-free carrier-phase observations after detecting cycle slip to estimates the ionospheric delay using a spherical ionospheric shell model, in which the vertical delays are described by means of a zenith delay at the station position and latitudinal and longitudinal gradients. Geometry-free carrier-phase observations were applied to avoid unwanted effects of pseudorange measurements, such as code multipath. The ionospheric estimation in this algorithm is performed by means of sequential least-squares adjustment. Finally, an adaptable user interface MATLAB software are capable of estimating ionosphere delay, ambiguity term and ionosphere gradient accurately.

Precise total electron content (TEC) is required to produce accurate spatial and temporal resolution of global ionosphere maps (GIMs). Receivers and satellite differential code biases (DCBs) are one of the main error sources in estimating precise TEC from Global Positioning System (GPS) data. Recently, researchers have been interested in developing models and algorithms to compute DCBs of receivers and satellites close to those computed from the Ionosphere Associated Analysis Centers (IAACs). Here we introduce a MATLAB code called Multi Station DCB Estimation (MSDCBE) to calculate satellite and receiver DCBs from GPS data. MSDCBE based on a spherical harmonic function and a geometry-free combination of GPS carrier-phase, pseudo-range code observations, and weighted least squares was applied to solve observation equations and to improve estimation of DCB values. There are many factors affecting the estimated values of DCBs. The first one is the observation weighting function which depends on the satellite elevation angle. The second factor is concerned with estimating DCBs using a single GPS station using the Zero Difference DCB Estimation (ZDDCBE) code or using the GPS network used by the MSDCBE code. The third factor is the number of GPS receivers in the network. Results from MSDCBE were evaluated and compared with data from IAACs and other codes like M_DCB and ZDDCBE. The results of weighted (MSDCBE) least squares show an improvement for estimated DCBs, where mean differences from the Center for Orbit Determination in Europe (CODE) (University of Bern, Switzerland) are less than 0.746 ns. DCBs estimated from the GPS network show better agreement with IAAC than DCBs estimated from precise point positioning (PPP), where the mean differences are less than 0.1477 and 1.1866 ns, respectively. The mean differences of computed DCBs improved by increasing the number of GPS stations in the network.

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.