Precise point positioning technique uses recursive algorithms to solve the navigation
problem. In fact, traditional least square method doesn’t meet the requiredassessments of
processing speed, and quality in different geodetic and surveying applic
ations,due tobig
amount of output processing data provided by global navigation satellite systems.
Extended Kalman filter is considered as optimal solution approach of the navigation
problem. This filter requiresthe knowledge of measurements, its observational models, and
physical state for estimation problem like: (receiver dynamic, received signals characters,
and suitable estimation of its initial conditions).
Research refers to a mathematical suggestion, which reduce the negative effect of
convergence time at EKF initial conditions. This work also shows how a position
estimation accuracy affected by the suggested modification of using EKF in PPP, and
supporting the use ofthis modification in position estimation field, in spite of increasing
processing time.
The most important problem is to reduce radar signal noise ,so we must design a
pattern to process signal from source to destination. We might study target tracking by
using Kalmen filters which estimate the exact statistical average signals and pu
t a
mathematical relationship to the optimal detection technique for radar system with two
variations (position, velocity) and three variations (position, velocity, acceleration). Then,
we compare these filters by Matlab to check the least value for the studied error.