CN0 Estimator Based on the Adaptive Strong Tracking Kalman Filter for GNSS Vector Receivers The carriertonoise ratio (CN0) is an important indicator of the signal quality of global navigation satellite system receivers. In a vector receiver, estimating CN0 using a signal amplitude Kalman filter is a typical method. However, the classical Kalman filter (CKF) has a significant estimation delay if the signal power levels change suddenly. In a weak signal environment, it is difficult to estimate the measurement noise for CKF correctly. This article proposes the use of the adaptive strong tracking Kalman filter (ASTKF) to estimate CN0. The estimator was evaluated via simulation experiments and a static field test. The results demonstrate that the ASTKF CN0 estimator can track abrupt variations in CN0 and the method can estimate the weak signal CN0 correctly. When CN0 jumps, the ASTKF estimation method shows a significant advantage over the adaptive Kalman filter (AKF) method in terms of the time delay. Compared with the popular CN0 algorithms, the narrowtowideband power ratio (NWPR) method, and the variance summing method (VSM), the ASTKF CN0 estimator can adopt a shorter averaging time, which reduces the hysteresis of the estimation results.

An Improved AmbiguityFree Method for Precise GNSS Positioning with Utilizing Single Frequency Receivers The solution of carrier phase ambiguity is essential for precise global navigation satellite system (GNSS) positioning. Methods of searching in the coordinate domain show their advantage over the methods based on ambiguity fixing, for example, immune to cycle slips, far fewer epochs taken for obtaining the precise solution. However, there are still some drawbacks via using the Ambiguity Function Method (AFM), such as low computation efficiency and the existence of a false global optimum. The false global optimum is a situation where the Least Square (LS) criterion achieves minimum in another place than the point of the actual position, which restricts the application of this method to singlefrequency receivers. The numerical search approach derived in this paper is based on the Modified Ambiguity Function Approach (MAFA). It focuses on eliminating the false optimum solution and reducing the computation load by utilizing singlefrequency receivers without solving the ambiguity fixing problem. An improved segmented simulated annealing method is used to decrease the computation load while the Kernel Density Estimator (KDE) method is used to filter out the false optimum candidates. Static experiments were carried out to evaluate the performance of the new approach. It is shown that a precise result can be obtained by handling two epochs of data with z coordinate fixed to the referenced value. Meanwhile, the new approach can achieve a millimeter level of position accuracy after dealing with nineteen epochs of observations data when searching in                                                                                                                                                                  :354,domain. The new approach shows its robustness even if the search region is broad, and the prior position is several meters away from the referenced value.