An Improved GMP Based Localization Algorithm for Unknown Target Population Environments

In order to improve the identification performance under unknown target population conditions, a new greedy matching pursuit algorithm (GMP) based localization algorithm is proposed. First of all, based on the possible target position estimations by traditional GMP algorithm, a redefined threshold is proposed to choose more possible target positions from the remaining grids. So the missing probability can be improved. Afterwards, the least square (LS) method is utilized to remove several outliers of the target position estimations and then the false alarm probability can be reduced. Simulation results illustrate that the proposed algorithm has better target identification ability than traditional GMP approach in unknown target population scenarios.