scholarly journals Effects of Canopy and Multi-Epoch Observations on Single-Point Positioning Errors of a GNSS in Coniferous and Broadleaved Forests

2021 ◽  
Vol 13 (12) ◽  
pp. 2325
Author(s):  
Tong Feng ◽  
Shilin Chen ◽  
Zhongke Feng ◽  
Chaoyong Shen ◽  
Yi Tian
Keyword(s):  
Single Point ◽  
Positioning Error ◽  
Forest Resource ◽  
Forest Types ◽  
Positioning Accuracy ◽  
Forest Model ◽  
Single Point Positioning ◽  
Positioning Errors ◽  
Point Positioning ◽  
Positioning Time

Global navigation satellite systems (GNSS) can quickly, efficiently, and accurately provide precise coordinates of points, lines, and surface elements, plus complete surveys and determine various boundary lines in forest investigations and management. The system has become a powerful tool for dynamic forest resource investigations and monitoring. GNSS technology plays a unique and important role in estimating timber volume, calculating timber cutting area, and determining the location of virgin forest roads and individual trees in forests. In this study, we quantitatively analyzed the influence of crown size and observation time on the single-point positioning accuracy of GNSS receivers for different forest types. The GNSS located single points for different forest types and crown sizes, enabling the collection of data. The locating time for each tree was more than 10 min. Statistical methods were used to analyze the positioning accuracy of multi-epoch data, and a model was developed to estimate the maximum positioning errors under different forest conditions in a certain positioning time. The results showed that for a continuous positioning time of approximately 10 min, the maximum positioning accuracies in coniferous and broadleaf forests were obtained, which were 12.13 and 15.11 m, respectively. The size of a single canopy had no obvious influence on the single-point positioning error of the GNSS, and canopy density was proven to be closely related to the positioning accuracy of a GNSS. The determination coefficients (R2) in the regression analysis of the general model, coniferous forest model, and broadleaved forest model that were developed in this study were 0.579, 0.701, and 0.544, respectively. These results indicated that the model could effectively predict the maximum positioning error in a certain period of time under different forest types and crown conditions at middle altitudes, which has important guiding significance for forest resource inventories and precise forest management.

On the improvements of the single point positioning accuracy with Locata technology

GPS Solutions ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 273-282 ◽  
Author(s):  
Jean-Philippe Montillet ◽  
Lukasz K. Bonenberg ◽  
Craig M. Hancock ◽  
Gethin W. Roberts
Keyword(s):  
Single Point ◽  
Positioning Accuracy ◽  
Single Point Positioning ◽  
Point Positioning

scholarly journals Bidirectional Sliding Window for Boundary Recognition of Pavement Construction Area Using GPS-RTK

2020 ◽  
Vol 10 (4) ◽  
pp. 1277 ◽  
Author(s):  
Tong Xu ◽  
Siwei Chen ◽  
Dong Wang ◽  
Weigong Zhang
Keyword(s):  
Single Point ◽  
Sliding Window ◽  
Straight Line Segment ◽  
Distribution Models ◽  
Positioning Accuracy ◽  
Single Point Positioning ◽  
Boundary Recognition ◽  
Gps Rtk ◽  
Point Positioning ◽  
Pavement Construction

Unmanned pavement construction is of great significance in China, and the primary issue to be solved is how to identify the boundaries of the Pavement Construction Area (PCA). In this paper, we present a simple yet effective method, named the Bidirectional Sliding Window (BSW) method, for PCA boundary recognition. We first collected the latitude and longitude coordinates of the four vertices of straight quadrilaterals using the Global Positioning System—Real Time Kinematic (GPS-RTK) measurement principle for precise single-point positioning, analyzed single-point positioning accuracy, and determined the measurement error distribution models. Next, we took points at equal intervals along one straight line segment and two curved line segments with curvature radii of 70 m to 300 m, for simulation experiments. BSW was adopted to recognize the Possible Irrelevant Points (PIP) and Relevant Points (RP), which were used to identify PCA boundaries. Experiments show that when the proposed BSW algorithm is used and the single-point positioning accuracy is at the centimeter level, PCA boundary recognition for straight polygons reaches single-point positioning accuracy, and that for curved polygons reaches decimeter-level accuracy.

scholarly journals Reducing the Effect of Positioning Errors on Kinematic Raw Doppler (RD) Velocity Estimation Using BDS-2 Precise Point Positioning

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3029 ◽  
Author(s):  
Duan ◽  
Sun ◽  
Ouyang ◽  
Chen ◽  
Shi
Keyword(s):  
Precise Point Positioning ◽  
Single Point ◽  
Estimation Method ◽  
Satellite System ◽  
Velocity Estimation ◽  
Navigation Satellite ◽  
Positioning Accuracy ◽  
Positioning Errors ◽  
Point Positioning ◽  
Global Navigation Satellite

In the traditional raw Doppler (RD) velocity estimation method, the positioning error of the pseudorange-based global navigation satellite system (GNSS) single point positioning (SPP) solution affects the accuracy of the velocity estimation through the station-satellite unit cosine vector. To eliminate the effect of positioning errors, this paper proposes a carrier-phase-based second generation of the BeiDou navigation satellite system (BDS-2) precise point positioning (PPP) RD velocity estimation method. Compared with the SPP positioning accuracy of tens of meters, the BDS-2 kinematic PPP positioning accuracy is significantly improved to the dm level. In order to verify the reliability and applicability of the developed method, three dedicated tests, the vehicle-borne, ship-borne and air-borne platforms, were conducted. In the vehicle-borne experiment, the GNSS and inertial navigation system (INS)-integrated velocity solution was chosen as the reference. The velocity accuracy of the BDS-2 PPP RD method was better than that of SPP RD by 28.4%, 27.1% and 26.1% in the east, north and up directions, respectively. In the ship-borne and air-borne experiments, the BDS-2 PPP RD velocity accuracy was improved by 17.4%, 21.4%, 17.8%, and 38.1%, 17.6%, 17.5% in the same three directions, respectively, compared with the BDS-2 SPP RD solutions. The reference in these two tests is the real-time kinematic (RTK) Position Derivation (PD)-based velocity.

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