Concerning the problem that low-cost Dual-antenna Attitude determination System (DAS) has low accuracy and gross error because of using direct solution, an improved algorithm based on carrier phase and pseudo-range double-difference Real-Time Kinematic (RTK) Kalman filter was proposed. Firstly, the baseline length was employed as the observation, then the precise baseline length obtained in advance was taken as the observation error. Secondly, the position of master antenna was corrected according to the epoch time interval of the slave antenna receiver and the integer ambiguity was solved by MLABMDA (Modified LABMDA) algorithm. Experimental results in static and dynamic mode show that the accuracy of the heading angle calculated by the proposed algorithm is about 1 degree and the calculated pitch angle accuracy is about 2-3 degrees in the case of baseline length 1.1 m with GPS and Beidou dual systems. The proposed algorithm improves the robustness and accuracy of the system greatly compared with the traditional dual-antenna attitude determination by direct solution.

Focusing on the issues that large fluctuation of Received Signal Strength Indication (RSSI), complex clustering of fingerprint database and large positioning error in traditional iBeacon fingerprinting, a new Bluetooth localization algorithm based on sort feature matching and distance weighting was proposed. In the off-line stage, the RSSI vector size was used to generate the sorting characteristic code. The generated code combined with the information of the position coordinates constituted the fingerprint information, to form the fingerprint library. While in the online positioning stage, the RSSI was firstly weighted by sliding window. Then, indoor pedestrian positioning was achieved by using the sort eigenvector fingerprint matching positioning algorithm and distance-based optimal Weighted K Nearest Neighbors (WKNN). In the localization simulation experiments, the feature codes were used for automatical clustering to reduce the complexity of clustering with maximum error of 0.952 m of indoor pedestrian localization.