الگوريتم بهينه شده kNN مبتني بر RSSI جهت مكان يابي هدف در محيط هاي داخلي

RSSI-Based Modified K Nearest Neighbors Algorithm For Indoor Target Tracking

Since global positioning system is faced high error in indoor target tracking, indoor target tracking methods developed. The high errors arise from multipath propagation which is main problem in indoor tracking. Among different indoor tracking methods with high complexity K Nearest Neighbors algorithm is used in this paper to lower the complexity and heighten the accuracy. This algorithm works based on Radio Signal Strength Indication; also some changes have been applied on this algorithm so we named it modified K Nearest Neighbors algorithm. The change includes a novel weight vector that depends on the strength of signal which surge this scheme’s accuracy. At the same time to mitigate the effects of multipath propagation, multichannel is used and its influence is shown in the simulation. Formulation in this system is simple and different calibration is done to increase the precision. The simulations prove this scheme as a high accurate indoor tracking scheme as below one meter error in a 100 square meters environment.

Since global positioning system is faced high error in indoor target tracking, indoor target tracking methods developed. The high errors arise from multipath propagation which is main problem in indoor tracking. Among different indoor tracking methods with high complexity K Nearest Neighbors algorithm is used in this paper to lower the complexity and heighten the accuracy. This algorithm works based on Radio Signal Strength Indication; also some changes have been applied on this algorithm so we named it modified K Nearest Neighbors algorithm. The change includes a novel weight vector that depends on the strength of signal which surge this scheme’s accuracy. At the same time to mitigate the effects of multipath propagation, multichannel is used and its influence is shown in the simulation. Formulation in this system is simple and different calibration is done to increase the precision. The simulations prove this scheme as a high accurate indoor tracking scheme as below one meter error in a 100 square meters environment.