No abstract

With the development of the information age and the maturity of Internet of Things technology, wireless sensor network has been widely applied in indoor localization. However, the non-line-of-sight (NLOS) propagation in complicated environment and the inherent noise of the sensor will introduce errors in the measurements, which will seriously lead to inaccurate positioning. In this paper, a novel localization scheme based on the mean reconstruction method is proposed, which reconstructs the distance measurements from all beacon nodes by taking the average twice to weaken the adverse effects of NLOS. At the same time, the noise average is re-estimated when the distance difference is not too large. Next, the robust extended Kalman filter (REKF) is used to process the reconstructed distance measurements to obtain positioning results. To make the positioning results more accurate, hypothesis test is used as NLOS identification to classify the position estimates generated from all distance combinations by least-squares. Then, the residual weighting (RWGH) method is utilized to combine the position estimates that fall into the validation region. At last, we merge the results from RWGH and REKF. The simulation and experimental results show that the proposed algorithm has high positioning accuracy and strong positioning robustness.

The indoor environment is intricate and the global positioning system (GPS) unable to satisfy the demand of indoor location accuracy. Therefore, the localization method based on wireless sensor network (WSN) has attach great importance and researched lately. The toughest issue to solve is the non-line of sight (NLOS) error caused by obstacles and other reasons. Hence, a location method based on hypothesis test and modified fuzzy probabilistic data association filter (HT-MFDAF) is proposed in this paper. Line-of-sight (LOS) and NLOS situations are regarded as an interactive Markov process. In the case of NLOS, we firstly identify and mitigate NLOS based on hypothesis testing theory. Then the ones which still have serious NOLS pollution is discarded by calculating similarity. Finally the fuzzy membership degree is calculated by MFDAF, reconstructing the correlation probability to get the position estimate. The eventual location result is acquired by the Interactive Multiple Model (IMM) which weighted LOS and NLOS estimated position. Simulation and experimental results demonstrate the effectiveness of the algorithm.

With the development of information age and the maturity of Internet of things (IoT) technology, wireless sensor network (WSN) has been widely applied in indoor localization. However, the non-line-of-sight (NLOS) environment and the inherent noise of the sensor will introduce errors in the measurement, which will seriously lead to inaccurate positioning. In this paper, a novel localization scheme based on mean reconstruction method is proposed, which reconstructs the distance measurements from each beacon nodes by taking the average twice to weaken the adverse effects of NLOS. At the same time, the noise average is re-estimated when the distance difference is tiny. Next, the robust extended Kalman filter (REKF) is used to process the reconstructed distance measurements to obtain positioning results. In order to prevent invalid reconstruction, hypothesis testing is used as NLOS identification to classify the position estimates which generated from all distance combinations by least-squares. Then we use residual weighting (RWGH) method to combine position estimates that fall into validation gate, and take it as a collaborative localization with the REKF algorithm. The simulation and experimental results show that the proposed algorithm has high positioning accuracy and strong positioning robustness.