Indoor Tdoa Tracking Using Robust Measurement Filtering Subtitle

Indoor positioning technology plays an important role in human's daily life.The signal often subjects to non-line-of-sight(NLOS)problems during the propagation process in indoor environments due to the presence of obstacles and walls,the obtained signal may be corrupted by large errors,degrading the positioning accuracy seriously.High accuracy target positioning and tracking using time difference of arrival(TDOA)in indoor environments is researched in this paper.A two-step algorithm with a structure of "preprocessing-positioning" is reviewed first.It adopts a robust Kalman filter(RKF)to preprocess the TDOA,which can mitigate the large NLOS errors in TDOA measurements effectively.A recursively bounded grid-based filter(RBGF)is adopted then to realize target tracking using the preprocessed TDOA as input.Next,the research of positioning information feedback based enhanced TDOA preprocessing is carried out on the basis of the two-step algorithm.The main works include;1.The probability density function truncation based enhanced TDOA preprocessing and target tracking.It adopts the RKF to preprocess the measured TDOA,then uses the possible target area to establish inequality constraints on the true TDOA value,the Gaussian probability density function of the TDOA preprocessing result obtained by the RKF is truncated outside the inequality constraints to realize the enhanced TDOA preprocessing.Higher target tracking accuracy is achieved by inputting the enhanced TDOA preprocessing result into the RBGF.2.The Kullback-Leibler(KL)divergence minimization based enhanced TDOA preprocessing and target tracking.It uses KL divergence as the similarity measure between two Gaussian distributions,aims at finding a Gaussian posterior that is closest to the Gaussian posterior of the preprocessing result of the RKF while satisfying all the inequality constraints,the found Gaussian posterior is then treated as the enhanced TDOA preprocessing result.Combined with RBGF,a higher target tracking accuracy is achieved.An initial bound determination method based on two-step weighted least squares positioning is proposed to solve the problem of huge amount of calculation of RBGF at the initial moment,it can reduce the amount of calculation at the initial moment greatly.The probability density function truncation based enhanced TDOA preprocessing algorithm and the Kullback-Leibler(KL)divergence minimization based enhanced TDOA preprocessing algorithm further improve the TDOA preprocessing performance on the basis that the RKF can mitigate NLOS errors effectively.Combined with the RBGF,higher target tracking accuracy is achieved over existing methods.The simulation experiments proves the effectiveness of the proposed algorithms.