Research Article
An Improved GMP Based Localization Algorithm for Unknown Target Population Environments
@INPROCEEDINGS{10.4108/eai.15-8-2015.2261079, author={Bao Chen and Jun Yan and Xiaofu Wu and Wei-ping Zhu}, title={An Improved GMP Based Localization Algorithm for Unknown Target Population Environments}, proceedings={10th EAI International Conference on Communications and Networking in China}, publisher={IEEE}, proceedings_a={CHINACOM}, year={2015}, month={9}, keywords={compressed sensing; target localization; the false alarm probability; the missing probability; greedy matching pursuit (gmp) algorithm}, doi={10.4108/eai.15-8-2015.2261079} }- Bao Chen
Jun Yan
Xiaofu Wu
Wei-ping Zhu
Year: 2015
An Improved GMP Based Localization Algorithm for Unknown Target Population Environments
CHINACOM
IEEE
DOI: 10.4108/eai.15-8-2015.2261079
Abstract
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.