An effective QR-based reduction algorithm for the fast estimation of GNSS high-dimensional ambiguity resolution

Lu, Linfeng; Liu, W.; Zhang, X.

doi:10.1080/00396265.2016.1230953

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…Using more observations inevitably increases the AR dimension. As the number of AR dimensions increases, the difference in the size of the residual quadratic corresponding to the optimal candidate solution and the suboptimal candidate solution of ambiguity becomes less significant, and the ratio value gets close to 1.0, gradually [44]. Therefore, the commonly adopted empirical threshold of 3.0 is too strict for high-dimension AR, and it is easy to reject the ambiguity that can be fixed correctly.…”

Section: Data Processing Strategymentioning

confidence: 99%

BDS/GPS Multi-Baseline Relative Positioning for Deformation Monitoring

Wang

Dai

2022

Remote Sensing

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The single-baseline solution (SBS) model has been widely adopted by the existing global navigation satellite system (GNSS) deformation monitoring systems due to its theoretical simplicity and ease of implementation. However, the SBS model neglects the mathematical correlation between baselines, and the accuracy and reliability can be degraded for baselines with long length, large height difference or frequent satellite signal occlusion. When monitoring large-area ground settlement or long-spanned linear objects such as bridges and railroads, multiple reference stations are frequently utilized, which can be exploited to improve the monitoring performance. Therefore, this paper evaluates the multi-baseline solution (MBS) model, and constrained-MBS (CMBS) model that has a prior constraint of the spatial-correlated tropospheric delay. The reliability and validity of the MBS model are verified using GPS/BDS datasets from ground settlement deformation monitoring with a baseline length of about 20 km and a height difference of about 200 m. Numerical results show that, compared with the SBS model, the MBS model can reduce the positioning standard deviation (STD) and root-mean-squared (RMS) errors by up to (47.4/51.3/66.2%) and (56.9/60.4/58.4%) in the north/east/up components, respectively. Moreover, the combined GPS/BDS positioning performance for the MBS model outperforms the GPS-only and BDS-only positioning models, with an average accuracy improvement of about 13.8 and 25.8%, with the highest accuracy improvement of about 41.6 and 43.8%, respectively. With the additional tropospheric delay constraint, the CMBS model improves the monitoring precision in the up direction by about 45.0%.

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Section: Data Processing Strategymentioning

confidence: 99%

BDS/GPS Multi-Baseline Relative Positioning for Deformation Monitoring

Wang

Dai

2022

Remote Sensing

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“…However, too many satellites per epoch will jeopardize both fast ambiguity search and reliable ambiguity validation (Verhagen et al 2012). To be specific, the search for the integer candidates will slow down or even get stuck on the occasion of several tens of ambiguities injected simultaneously into LAMBDA (e.g., Jazaeri et al 2012;Lu et al 2018); even if smoothly through LAMBDA, the integer candidate validation for high-dimensional ambiguities through the ratio tests often malfunctions due to the hard choice of threshold values. Figure 10 shows the mean ratio values over the 31 days at all 107 stations against the number of resolved ambiguities.…”

Section: Instantaneous Ppp-war On a Mobile Vehiclementioning

confidence: 99%

Toward global instantaneous decimeter-level positioning using tightly coupled multi-constellation and multi-frequency GNSS

Geng

Guo

Chang

et al. 2018

J Geod

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Autonomous driving represents one of the emerging applications that require both high-precision positions and highly critical timeliness to reach stringent safety standards. We develop a method to potentially achieve global instantaneous decimeter-level positioning by virtue of tightly coupled multi-GNSS triple-frequency observations contributing to precise point positioning (PPP). Inter-system phase biases (ISPBs) for two wide-lane observables are first computed for each station to form inter-GNSS resolvable ambiguities, and then correspondingly two wide-lane fractional-cycle biases (FCBs) are computed for each satellite to recover the integer property of single-station ambiguities. With both ISPB and FCB products, we can accomplish tightly coupled multi-GNSS PPP wide-lane ambiguity resolution (PPP-WAR) using only a single epoch of triple-frequency observations on a global scale. To verify this method, we used 1 month of GPS/BeiDou/Galileo/QZSS data from 107 globally distributed stations and 1 h of such multi-GNSS data collected on a vehicle moving in an urban area. We found that both ISPB and FCB products could be estimated every 24 h with high precisions of around or below 0.1 cycles; 83-98% of their day-today variations fell within 0.1 cycles, facilitating their precise predictions for real-time applications. Using these corrections, we achieved both instantly and reliably ambiguity-fixed solutions at 91.2% of all epochs at the 107 stations on average; the resultant single-epoch positions reached a mean accuracy of 0.22 m, 0.18 m and 0.63 m for the east, north and up components, respectively, in case of abundant triple-frequency observations from over 15 satellites. Similarly, for the vehicle-borne test, we obtained instantaneous PPP-WAR solutions at 99.31% of all epochs and achieved a positioning accuracy of 0.29, 0.35 and 0.77 m for the east, north and up components, respectively, which improved significantly the identification of road lanes as opposed to other single-epoch solutions. Finally, we expect that the prospect of instantaneous PPP-WAR in aiding driverless vehicles can be more promising if, whenever possible, integrated with inertial sensors and/or smoothed through multi-epoch data.

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