The non-Gaussian observation error is a threat for advanced receiver autonomous integrity monitoring (ARAIM), because the protection level of ARAIM based on the Gaussian distribution assumption is insufficient to envelope the positioning error (PE), and the probability of hazardously misleading information (PHMI) is difficult to be satisfied. The traditional non-Gaussian overbounding method is limited by the correlation among observation errors, and the deteriorated continuity risk resulting from the conservative inflation factor for overbounding, simultaneously. We propose an enhanced ARAIM method by position-domain non-Gaussian error overbounding. Furthermore, the upper bound of the inflation factor is imposed to release the conservativeness of overbounding. The simulation and the real-world data are utilized to test the proposed method. The simulation experiment has shown that the global worldwide availability level can be increased to 99.99% by using the proposed method. The real-word data experiment reveals that the proposed method can simultaneously satisfy the integrity risk and continuity risk with the boundary of the inflation factor.

Integrity monitoring of precise point positioning (PPP) can provide tightly guaranteed absolute position error bounds for safety-critical applications. However, a complex local environment makes PPP integrity monitoring much more challenging, such as urban canyons. Significant prone multipaths and low observation redundancy are the main difficulties in the accuracy and reliability of PPP. Therefore, we proposed a solution separation-based integrity monitoring algorithm, which is based on a single and dual frequency undifferenced and uncombined PPP model considering compensation for the multipath error distortion by Gaussian overbounding. Both the static and the kinematic data are utilized to test the proposed algorithm. The results show that the proposed algorithm can produce an adequate protection level (PL) in horizontal and vertical directions. Furthermore, the proposed algorithm can obtain a smoother PL and positioning error in a dynamic local environment, and effectively suppress misleading information.

The re-initialization of precise point positioning (PPP) can be avoided by cycle slip detection and correction. Ionospheric delay is critical for cycle slip detection and correction, especially for a long data gap. The frequency diversity from GNSS modernization provides the potential for mitigating the impact of ionospheric delay on cycle slip detection and correction. The proposed method constructs the extra-wide lane (EWL), the wide lane (WL), and the narrow lane (NL) epoch-differenced linear combinations based on the ionosphere constrain criterion, so as to determine the undifferenced cycle slips from the cascading ambiguity resolution. The experiment results show that the cycle slips can be fixed correctly even though cycle slips occur in all the available carrier phase observations, and the 3 min data gaps can be merged without high precision positioning continuity loss. The kinematic experiment shows that the instantaneous re-initialization can be achieved with the proposed method.

Real-time precise point positioning (RT-PPP) relies on the real-time service as satellite orbit and clock corrections for users to achieve decimeter or centimeter-level positioning. Although the real-time service is nearly always with high accuracy, it will inevitably have abnormal values or accuracy degradation. The faults from satellite orbit and clock corrections must be detected to provide a reliable positioning performance. In this contribution, a state-domain based integrity monitoring method of satellite orbit and clock corrections for RT-PPP is proposed. The prediction models for satellite orbit and clock correction parameters are constructed by analyzing the daily variation statistical characteristics of correction parameters in the state domain. After compensation for non-ideal error distribution by overbounding, the detection statistics and minimum detection errors are established based on the corrected precision satellite orbit and clock comprehensive errors, which aims to monitor the quality of satellite corrections. The experiment is used to test the false alarm and missed detection performance of the proposed method. The results confirm that the abnormal satellite orbit and clock corrections can be detected accurately with given missed detection rate 1×10 ^-3 , and false alarm rate 1×10 ^-5 . Moreover, using the proposed monitoring method, the RT-PPP results after excluding abnormal satellite orbit and clock corrections can be improved by an average of 6.51%, 13.94% and 8.14% in the east, north and up directions, respectively.

Multi-GNSS PPP partial ambiguity resolution (PAR) can improve the fixing success rate and shorten the time to first fix (TTFF). Ambiguity subset selection based on the bootstrapping success rate sorting criterion (BSSC) is widely used in PPP PAR due to its ease of computation and comprehensive evaluation of the global quality of ambiguity solutions. However, due to the influence of unmodeled errors, such as atmospheric residuals and gross errors, ambiguity parameter estimation will inevitably introduce bias. For ambiguity parameters with bias, their variance will converge incorrectly and will not accurately reflect the estimation accuracy. As a result, the selected ambiguity subset based on the BSSC becomes inaccurate, affecting the fixing success rate and TTFF. Therefore, we proposed an improved multi-GNSS PPP PAR method based on a two-step sorting criterion (TSSC). This method aims to address the influence of inaccurate variance of ambiguity parameters, particularly those with low observation quality, on the ambiguity subset selection based on the BSSC. The ambiguity subset satisfying the preset success rate threshold is selected to reduce the influence of unconverged ambiguity on the TSSC. In the first step of the sorting process, the observations whose elevation angle is below 30° or whose posterior residual falls into the IGG3 model reduction domain are clustered together. The posterior observation weight criterion (POWC) instead of the BSSC is adopted to sort ambiguities to overcome the false convergence of variance of ambiguity parameters. In the second step of the sorting process, the remaining ambiguities with reasonable variances are sorted based on the BSSC. Finally, the bottom ambiguity is removed one by one from the ambiguity subset sorted based on the two-step sorting criterion (TSSC) until the requirements of the ratio test for LAMBDA are met. The static data from 10 MGEX stations over a period of 30 days, along with urban kinematic data, were collected to validate the proposed method. Compared with the PAR based on the BSSC, the static experiments demonstrated a reduction of 8.7% and 16.8% in the TTFF and convergence time, respectively. Additionally, the positioning accuracy in the east, north, and up directions was improved by 20.1%, 17.1%, and 4.67%, respectively. Furthermore, the kinematic experiment revealed that the TTFF and convergence time decreased from 1.65 min and 10.5 min to 1.3 min and 1.8 min, respectively, with higher positioning accuracy.