Integrity monitoring for undifferenced and uncombined PPP under local environmental conditions

Abstract

Integrity monitoring of precise point positioning (PPP) can provide tightly guaranteed absolute position error bounds for safety-critical applications. However, complex local environment makes PPP integrity monitoring much more challenging, such as urban canyons. Significant prone multipaths and low observation redundancy are main difficulties to the accuracy and the reliability of PPP. Therefore, we proposed a solution separation-based integrity monitoring algorithm, which is based on a single and dual frequency-mixed 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 adequate protection level in horizontal and vertical directions. Furthermore, the proposed algorithm can obtain smoother protection level and positioning error under the dynamic local environment, and effectively suppress the misleading information.

Characterizing the fault performance of real-time precise satellite orbit and clock correction products

“…In order to reveal the sensitivity of fault detection, we conducted the comparison experiment between the thresholds of the MPE-COB and the MPE-NB, as shown in figure 5. The calculation of the threshold comes from (6).…”

“…In order to evaluate the fault performance of the correction products, we performed the fault detection experiment by comparing the MPE-COB with the MPE-NB. The fault detection model of MPE test statistics comes from (6). The fault detection results of two methods are shown in figure 6.…”

“…is required for PPP applications, especially for safety-related applications, such as urban traffic management and automatic driving systems [5,6]. The PPP integrity monitoring can be regarded as an extension of the traditional RAIM and ARAIM method, including the a priori fault probability contained in the broadcast integrity information [7,8].…”

Improved protection level for the solution-separation ARAIM based on worst-case fault bias searching

“…We assume that the positioning estimate error ε 0,q and detection statistics ∆ i,q in ARAIM are modeled as Gaussian distribution. In fact, the actual observation noise does not strictly adhere to a Gaussian distribution [3]. In this paper, conservative Gaussian distribution parameters, such as the standard deviation of observation noise, will be selected to mitigate the impact of non-Gaussian characteristics.…”

“…classic RAIM algorithm is designed to satisfy the required navigation performance (RNP) of en-route and, in some states, non-precision approach phase, as defined by international civil aviation organization [2]. With the full deployment of Galileo and BDS, an increased number of redundant observations are available [3]. There has been a renewed interest in expanding the RAIM to satisfy more demanding RNP, such as localizer precision vertical 200 .…”