The application of indoor localization systems based on the improved Kalman filtering algorithm

In order to improve the accuracy of indoor positioning in wireless sensor network, an indoor localization algorithm based on improved Kalman filtering is proposed. By introducing suboptimal unbiased maximum a posteriori (MAP) noise statistical estimator, the system noise covariance and measurement noise covariance of Kalman algorithm is modified adaptively to replace Gaussian white noise sequence of zero mean difference and known covariance, which makes the algorithm have the good filtering effect. In order to show the performance of the proposed algorithm, the indoor localization algorithm performance is compared. The experiment result shows that the proposed algorithm can improve indoor positioning accuracy of unknown nodes.