A Non-Line-of-Sight mitigation localization algorithm for sensor networks using clustering analysis

When the number of corrupted measured distances is minor (e.g.: 3 corrupted from 10), all the pre-located estimators before MRDF processing distribute densely and coherently. Using MRDF processing accurate estimators can be got. When all measured distances are attached with the same NLOS errors ranging from 5% to 50% of each measured distance, MRDF algorithm has similar performances with LSE and performs better than RWGH and RMIN.