In order to take into account the compatibility and interoperability of the future Global Navigation Satellite System (GNSS), and solve the problem of low resolution efficiency of multi-frequency, multi-mode and high-dimensional ambiguity in conventional methods, based on the lattice theory, a Closest Lattice Point (CLP) search algorithm was proposed to search the ambiguity integer value. First, the ambiguity search was transformed into the closest lattice point search problem of the known lattice points in the lattice. Then, according to the lattice base specification, the lattice base vectors with the minimum possible length and orthogonal to each other were obtained. Finally, the CLP search algorithm was used to search the optimal ambiguity parameters. The results of simulation and testing data verify that, the proposed CLP search algorithm is theoretically more efficient and reliable on the resolution of ambiguity parameters compared to the classical Least squares AMBiguity Decorrelation Adjustment (LAMBDA) and Modified LAMBDA (MLAMBDA) algorithms, and it has the search time of each parameter stable at 0.01 seconds, which means even in high-dimensional case, the CLP search algorithm is still stable and reliable.

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.

Traditional carrier phase difference algorithm is not suitable for deformation monitoring, the accuracy of Real-Time Kinematic (RTK) positioning cannot meet requirements and static relative positioning based on carrier double-differential phase has poor deformation tracking performance with continuous calculations. To solve these problems, based on the deep research of dynamic and static algorithms, a dynamic and static adaptive fusion algorithm based on carrier phase difference was proposed. The convergence of positioning results was judged by variance-change method in real time, then the state priori estimation process of Extended Kalman Filter (EKF) was adaptively adjusted. In the process, the covariance value of priori estimation error of position parameters was increased at the convergence time, so that the posteriori process of EKF tended to trust measured value. EKF iteration was used at the non-convergence time, so that the posteriori process of EKF tended to trust state predicted value. The experimental results show that compared with traditional RTK, the accuracy of the new algorithm is improved, with horizontal accuracy of ±2 mm, and altitudinal accuracy of ±4 mm. Compared with static positioning, the observation period is reduced, and the tracking performance of micro-deformation is improved.

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.

In the low-cost aircraft attitude detection system, the complementary filter was widely used because of simple principle and low computational complexity. Aiming at the problem that the accelerometer can not distinguish between the acceleration of gravity and motion may cause attitude calculation error by complementary filtering, an attitude calculation algorithm of complementary and adaptive limited filter was proposed for low cost aircraft, and the design method of adaptive limited filter gate valve was given. The angular velocity output by a gyroscope and the acceleration output by an accelerometer were fused to obtain the gate valve of the limited filter, and then the normalized output increment of the accelerometer by limiting filter replaced the accelerometer input of the original complementary filter. The accuracy of attitude determination under uniform motion was improved. The actual system test shows that the proposed algorithm has high accuracy and low cost, and is easy to be realized in low cost aircraft control system.