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doi: 10.12265/j.gnss.2024-04daodu
Abstract:
2024, 49(3): 1-9.
doi: 10.12265/j.gnss.2024052
Abstract:
The high accuracy service (HAS) of the Galileo system uses E6B signals to broadcast free precision satellite orbit, clock deviation and code deviation correction information to users around the world, which is of great significance in the field of high-precision real-time satellite applications. Evaluating the spatial signal error and precise point positioning performance is an important prerequisite for using HAS well. Based on the HAS orbital clock products from December 1 to 31, 2023, the root mean square errors (RMSE) of the Galileo satellite orbit products were 4.16 cm, 8.42 cm and 7.62 cm in the radial (R), tangential (A) and normal (C) directions, and 4.52 cm, 9.99 cm and 7.07 cm in GPS, respectively, while the standard deviation (STD) of the Galileo clock products was 0.16 ns and 0.26 ns for GPS. Precise point positioning (PPP) was carried out based on the observation data of 10 stations in Europe. The results show that the positioning accuracy of Galileo in the east (E), north (N) and zenith (U) directions is 6.44 cm, 4.65 cm and 11.36 cm, GPS is 7.42 cm, 5.78 cm and 12.04 cm, and the joint positioning is 4.11 cm, 3.10 cm and 7.56 cm, respectively. The results indicate that HAS can meet the high-precision positioning requirements.
The high accuracy service (HAS) of the Galileo system uses E6B signals to broadcast free precision satellite orbit, clock deviation and code deviation correction information to users around the world, which is of great significance in the field of high-precision real-time satellite applications. Evaluating the spatial signal error and precise point positioning performance is an important prerequisite for using HAS well. Based on the HAS orbital clock products from December 1 to 31, 2023, the root mean square errors (RMSE) of the Galileo satellite orbit products were 4.16 cm, 8.42 cm and 7.62 cm in the radial (R), tangential (A) and normal (C) directions, and 4.52 cm, 9.99 cm and 7.07 cm in GPS, respectively, while the standard deviation (STD) of the Galileo clock products was 0.16 ns and 0.26 ns for GPS. Precise point positioning (PPP) was carried out based on the observation data of 10 stations in Europe. The results show that the positioning accuracy of Galileo in the east (E), north (N) and zenith (U) directions is 6.44 cm, 4.65 cm and 11.36 cm, GPS is 7.42 cm, 5.78 cm and 12.04 cm, and the joint positioning is 4.11 cm, 3.10 cm and 7.56 cm, respectively. The results indicate that HAS can meet the high-precision positioning requirements.
2024, 49(3): 10-19.
doi: 10.12265/j.gnss.2023214
Abstract:
Aiming at the availability of BDS-3 PPP-B2b service, a systematic analysis has been conducted towards the availability ratio of PPP-B2b corrections, the average number of satellites with available corrections and the matching characteristics of PPP-B2b corrections. The results show that in China and surrounding areas, the availability of BDS-3 PPP-B2b corrections is between 71% and 95%, and reaches the maximum in Beijing. While that for the GPS system is between 68.5% and 88.6%, which is worse than the BDS-3. The satellite observation arcs lack of PPP-B2b corrections are mainly concentrated in low elevation angle periods for users in China and surrounding areas, and the availability of PPP-B2b corrections will increase with the increase of cut-off elevation angle. The average number of satellites with available PPP-B2b corrections for BDS-3, GPS and BDS-3/GPS systems in China and surrounding areas is about 8, 7 and 15 respectively, which can ensure effective real-time PPP service performance. However, on average, about 1 BDS-3 satellite and 2 GPS satellites cannot participate in the real-time PPP positioning due to the lack of PPP-B2b corrections for users in the area. For areas south of the equator, BDS-3 only or GPS-only system is basically unable to provide effective real-time PPP services with the average availability of PPP-B2b correction being less than 50%, but the BDS-3/GPS dual system can provide about 7 to 11 available satellites in some low latitude areas. Users should be noticed that there will be a temporary mismatching state between the orbit and clock offset corrections each time when the IOD Corr parameter changes due to the different updating frequency.
Aiming at the availability of BDS-3 PPP-B2b service, a systematic analysis has been conducted towards the availability ratio of PPP-B2b corrections, the average number of satellites with available corrections and the matching characteristics of PPP-B2b corrections. The results show that in China and surrounding areas, the availability of BDS-3 PPP-B2b corrections is between 71% and 95%, and reaches the maximum in Beijing. While that for the GPS system is between 68.5% and 88.6%, which is worse than the BDS-3. The satellite observation arcs lack of PPP-B2b corrections are mainly concentrated in low elevation angle periods for users in China and surrounding areas, and the availability of PPP-B2b corrections will increase with the increase of cut-off elevation angle. The average number of satellites with available PPP-B2b corrections for BDS-3, GPS and BDS-3/GPS systems in China and surrounding areas is about 8, 7 and 15 respectively, which can ensure effective real-time PPP service performance. However, on average, about 1 BDS-3 satellite and 2 GPS satellites cannot participate in the real-time PPP positioning due to the lack of PPP-B2b corrections for users in the area. For areas south of the equator, BDS-3 only or GPS-only system is basically unable to provide effective real-time PPP services with the average availability of PPP-B2b correction being less than 50%, but the BDS-3/GPS dual system can provide about 7 to 11 available satellites in some low latitude areas. Users should be noticed that there will be a temporary mismatching state between the orbit and clock offset corrections each time when the IOD Corr parameter changes due to the different updating frequency.
2024, 49(3): 20-27.
doi: 10.12265/j.gnss.2024024
Abstract:
Aiming at the problem of high-precision positioning of mobile robot in Global Navigation Satellite System GNSS denied environment, An adaptive thresholding adaptive and generic accelerated segment test AGAST feature detection algorithm is proposed to improve the visual front-end of a vision/inertial guidance fusion localization system for mobile robots. The algorithm improves the visual odometry computation method by local histogram equalization and adaptive threshold detection, improves the quality of feature point extraction, and enhances the positioning accuracy and stability of visual odometry in complex environments. Visual odometry and inertial navigation system are fused based on factor graph optimization algorithm to realize high-precision positioning of mobile robot. The results show that, compared with the mainstream VINS-Mono algorithm, the proposed algorithm improves the positioning accuracy by 22.8% in the experiment of indoor data set and 59.7% in the experiment of outdoor data set, the proposed algorithm perform better than VINS-Mono algorithm in both two experiments and it can provide better positioning services for mobile robots.
Aiming at the problem of high-precision positioning of mobile robot in Global Navigation Satellite System GNSS denied environment, An adaptive thresholding adaptive and generic accelerated segment test AGAST feature detection algorithm is proposed to improve the visual front-end of a vision/inertial guidance fusion localization system for mobile robots. The algorithm improves the visual odometry computation method by local histogram equalization and adaptive threshold detection, improves the quality of feature point extraction, and enhances the positioning accuracy and stability of visual odometry in complex environments. Visual odometry and inertial navigation system are fused based on factor graph optimization algorithm to realize high-precision positioning of mobile robot. The results show that, compared with the mainstream VINS-Mono algorithm, the proposed algorithm improves the positioning accuracy by 22.8% in the experiment of indoor data set and 59.7% in the experiment of outdoor data set, the proposed algorithm perform better than VINS-Mono algorithm in both two experiments and it can provide better positioning services for mobile robots.
2024, 49(3): 28-37.
doi: 10.12265/j.gnss.2024002
Abstract:
The GNSS technology, as an important tool for mining subsidence monitoring, is significantly affected by the noise present in its time series. This paper proposes a denoising method that combines an Improved hybrid grey wolf particle swarm optimization (IPSOGWO) and an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), coupled with wavelet thresholding (WT). The IPSOGWO optimizes the hyperparameters of the ICEEMDAN algorithm to decompose the GNSS time series and extract the intrinsic mode functions (IMF). The multi-scale permutation entropy is used to select the IMF components containing noise. These components are then secondarily processed using wavelet thresholding and reconstructed with the remaining IMF components to obtain the denoised results. Experiments with simulated signals and actual data from an automated monitoring station in a mining area demonstrate that the proposed method outperforms the wavelet threshold, complete ensemble empirical mode decomposition (CEEMD), and GWO-ICEEMDAN in terms of denoising performance, providing reliable data for subsequent analysis of working face subsidence.
The GNSS technology, as an important tool for mining subsidence monitoring, is significantly affected by the noise present in its time series. This paper proposes a denoising method that combines an Improved hybrid grey wolf particle swarm optimization (IPSOGWO) and an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), coupled with wavelet thresholding (WT). The IPSOGWO optimizes the hyperparameters of the ICEEMDAN algorithm to decompose the GNSS time series and extract the intrinsic mode functions (IMF). The multi-scale permutation entropy is used to select the IMF components containing noise. These components are then secondarily processed using wavelet thresholding and reconstructed with the remaining IMF components to obtain the denoised results. Experiments with simulated signals and actual data from an automated monitoring station in a mining area demonstrate that the proposed method outperforms the wavelet threshold, complete ensemble empirical mode decomposition (CEEMD), and GWO-ICEEMDAN in terms of denoising performance, providing reliable data for subsequent analysis of working face subsidence.
2024, 49(3): 38-44.
doi: 10.12265/j.gnss.2024038
Abstract:
The 5th-Generation (5G) communication technology has brought new possibilities to the field of indoor positioning. Ultra-broadband (UWB) location technology, like 5G location, has the characteristics of large bandwidth and high frequency, but there are slight differences in positioning performance. To address the issues of poor accuracy and stability in single sensor positioning, a fusion positioning algorithm of 5G+UWB was studied in this paper. It establishes an indoor positioning system based on time difference of arrival (TDOA) for 5G, a UWB indoor positioning system based on trilateral positioning algorithm, and a 5G+UWB indoor positioning model based on the fusion positioning algorithm. The initial positioning results of each single system obtained through weighted least squares (WLS) algorithm are validated, followed by verification of improved positioning results obtained through Taylor series expansion method. Furthermore, experimental verification is conducted on the combined positioning results obtained by fusing the positioning results of the two single systems. It has been indicated by the experimental results that UWB single-system positioning shows lower accuracy but higher stability, while 5G single-system positioning exhibits higher accuracy but lower stability. After the combination of the two methods, the accuracy and stability of the combined system can be relatively high. The positioning accuracy of the combined system can be as high as 0.22 m and as low as 0.73 m, enabling sub-meter level positioning.
The 5th-Generation (5G) communication technology has brought new possibilities to the field of indoor positioning. Ultra-broadband (UWB) location technology, like 5G location, has the characteristics of large bandwidth and high frequency, but there are slight differences in positioning performance. To address the issues of poor accuracy and stability in single sensor positioning, a fusion positioning algorithm of 5G+UWB was studied in this paper. It establishes an indoor positioning system based on time difference of arrival (TDOA) for 5G, a UWB indoor positioning system based on trilateral positioning algorithm, and a 5G+UWB indoor positioning model based on the fusion positioning algorithm. The initial positioning results of each single system obtained through weighted least squares (WLS) algorithm are validated, followed by verification of improved positioning results obtained through Taylor series expansion method. Furthermore, experimental verification is conducted on the combined positioning results obtained by fusing the positioning results of the two single systems. It has been indicated by the experimental results that UWB single-system positioning shows lower accuracy but higher stability, while 5G single-system positioning exhibits higher accuracy but lower stability. After the combination of the two methods, the accuracy and stability of the combined system can be relatively high. The positioning accuracy of the combined system can be as high as 0.22 m and as low as 0.73 m, enabling sub-meter level positioning.
2024, 49(3): 45-50, 79.
doi: 10.12265/j.gnss.2023223
Abstract:
This study, based on eight years of data from 24 Global Navigation Satellite Systems (GNSS) stations in northern Germany, introduces the singular spectrum analysis method. It proposes a common mode error identification method that considers the inter-correlation of different residual subcomponents and their contribution rates. The impact of common mode errors on GNSS coordinate time series noise and parameter estimation is explored. Compared with the principal component analysis (PCA) method, it is found that the method proposed in this paper closely aligns with the common mode errors extracted by PCA, confirming the feasibility of the new method. The GNSS common mode error sequence mainly contains white noise, flicker noise, and power-law noise with non-integer spectral indices. After removing common mode errors, the magnitude of white noise and colored noise at each station decreased by an average of 30.32% and 52.61% respectively, indicating that colored noise dominates in common mode errors. Furthermore, after correcting common mode errors, the annual and semi-annual cycle amplitudes of coordinates are reduced, and the root mean square error of parameter fitting is decreased by 16.7%. In summary, the method described in this paper is of significant practical importance in improving the quality of GNSS coordinate time series.
This study, based on eight years of data from 24 Global Navigation Satellite Systems (GNSS) stations in northern Germany, introduces the singular spectrum analysis method. It proposes a common mode error identification method that considers the inter-correlation of different residual subcomponents and their contribution rates. The impact of common mode errors on GNSS coordinate time series noise and parameter estimation is explored. Compared with the principal component analysis (PCA) method, it is found that the method proposed in this paper closely aligns with the common mode errors extracted by PCA, confirming the feasibility of the new method. The GNSS common mode error sequence mainly contains white noise, flicker noise, and power-law noise with non-integer spectral indices. After removing common mode errors, the magnitude of white noise and colored noise at each station decreased by an average of 30.32% and 52.61% respectively, indicating that colored noise dominates in common mode errors. Furthermore, after correcting common mode errors, the annual and semi-annual cycle amplitudes of coordinates are reduced, and the root mean square error of parameter fitting is decreased by 16.7%. In summary, the method described in this paper is of significant practical importance in improving the quality of GNSS coordinate time series.
2024, 49(3): 51-56.
doi: 10.12265/j.gnss.2023157
Abstract:
Galileo already has 28 in-orbit satellites, with precise positioning capabilities on a global scale. All Galileo satellites are capable of broadcasting multi-frequency signals, and multi-frequency integration is expected to further improve the performance of precise point positioning (PPP) ambiguity-fixed solutions. In this paper, the fractional cycle bias (FCB) estimation method and ambiguity resolution (AR) method for Galileo triple-frequency uncombined (UC) PPP are developed, and the derived results are compared with those of dual-frequency UC PPP ambiguity-fixed and ambiguity-float solutions. The results indicate that the standard deviation (STD) of UC FCB series on a single frequency is better than 0.04 cycles using datasets from 155 globally distributed ground tracking stations. The convergence time of Galileo dual-frequency PPP float solutions in the east, north and up directions is 32.0 min, 10.0 min and 43.5 min, respectively, and the corresponding statistic of dual-frequency PPP fixed solutions is reduced to 30.5 min, 8.5 min and 32.0 min in the three directions, respectively. The convergence time of triple-frequency PPP fixed solutions is further shortened to 16.5 min, 8.0 min and 32.0 min in the three directions, respectively.
Galileo already has 28 in-orbit satellites, with precise positioning capabilities on a global scale. All Galileo satellites are capable of broadcasting multi-frequency signals, and multi-frequency integration is expected to further improve the performance of precise point positioning (PPP) ambiguity-fixed solutions. In this paper, the fractional cycle bias (FCB) estimation method and ambiguity resolution (AR) method for Galileo triple-frequency uncombined (UC) PPP are developed, and the derived results are compared with those of dual-frequency UC PPP ambiguity-fixed and ambiguity-float solutions. The results indicate that the standard deviation (STD) of UC FCB series on a single frequency is better than 0.04 cycles using datasets from 155 globally distributed ground tracking stations. The convergence time of Galileo dual-frequency PPP float solutions in the east, north and up directions is 32.0 min, 10.0 min and 43.5 min, respectively, and the corresponding statistic of dual-frequency PPP fixed solutions is reduced to 30.5 min, 8.5 min and 32.0 min in the three directions, respectively. The convergence time of triple-frequency PPP fixed solutions is further shortened to 16.5 min, 8.0 min and 32.0 min in the three directions, respectively.
2024, 49(3): 57-64.
doi: 10.12265/j.gnss.2023198
Abstract:
To enhance the performance of indoor localization using channel state information (CSI) fingerprints, an CSI image-based indoor localization method based on the improved MixNet model is proposed. In the offline phase, the method involves selecting the three access points (APs) with the highest received signal strength indication (RSSI) at the reference point (RP), extracting their CSI data, and converting it into image. Subsequently, the improved MixNet model is employed to train on these images and save the model. The improved MixNet model introduces coordinate attention (CA) and residual connections. Specifically, it replaces the squeeze-and-excitation (SE) attention in MixNet-s with CA to enhance the network’s information representation capability and extract CSI image fingerprint features more accurately. Moreover, it incorporates residual connections, tailored to the characteristics of the MixNet-s model, to enhance the network’s representation capacity and prevent overfitting. Finally, the network depth is reduced to ensure that all network layers are adequately trained. During the online phase, CSI data from the target device is collected and converted into image, and then input into the pre-trained improved MixNet model (named MixNet-CA). The final device position is estimated using a weighted centroid algorithm based on the model's output probabilities. The proposed method is validated in an indoor environment and achieve an average positioning error of 0.362 0 m.
To enhance the performance of indoor localization using channel state information (CSI) fingerprints, an CSI image-based indoor localization method based on the improved MixNet model is proposed. In the offline phase, the method involves selecting the three access points (APs) with the highest received signal strength indication (RSSI) at the reference point (RP), extracting their CSI data, and converting it into image. Subsequently, the improved MixNet model is employed to train on these images and save the model. The improved MixNet model introduces coordinate attention (CA) and residual connections. Specifically, it replaces the squeeze-and-excitation (SE) attention in MixNet-s with CA to enhance the network’s information representation capability and extract CSI image fingerprint features more accurately. Moreover, it incorporates residual connections, tailored to the characteristics of the MixNet-s model, to enhance the network’s representation capacity and prevent overfitting. Finally, the network depth is reduced to ensure that all network layers are adequately trained. During the online phase, CSI data from the target device is collected and converted into image, and then input into the pre-trained improved MixNet model (named MixNet-CA). The final device position is estimated using a weighted centroid algorithm based on the model's output probabilities. The proposed method is validated in an indoor environment and achieve an average positioning error of 0.362 0 m.
2024, 49(3): 65-72, 93.
doi: 10.12265/j.gnss.2024045
Abstract:
With the continuous improvement of smartphone chip and antenna performance, high-precision positioning based on mobile phones has gradually attracted widespread attention from academia and industry. Compared with single-point positioning, real-time kinematic (RTK) positioning usually shows higher positioning performance. However, there are still relatively few studies on the stochastic model of mobile phone RTK positioning. Therefore, this study takes Xiaomi 8 as an example to focus on the stochastic model of smartphones in a multi-system RTK positioning scenario. The research results show that different systems There are significant differences in satellite pseudorange noise, while the difference in phase noise is smaller. Based on this, this paper proposes a stochastic model of base station signal-to-noise ratio combined with comprehensive weighting between satellite systems, which is more efficient than the traditional signal-to-noise ratio model. The weight distribution between different systems was accurately considered. Static and kinematic experiments were conducted under open and occluded conditions. The results show that compared with the traditional signal-to-noise ratio model, the new model is RTK positioning accuracy in the three-dimensional direction under static openness, static occlusion, kinematic openness and kinematic occlusion has increased by 18.7%, 18.3%, 3.3% and 4.6% respectively.
With the continuous improvement of smartphone chip and antenna performance, high-precision positioning based on mobile phones has gradually attracted widespread attention from academia and industry. Compared with single-point positioning, real-time kinematic (RTK) positioning usually shows higher positioning performance. However, there are still relatively few studies on the stochastic model of mobile phone RTK positioning. Therefore, this study takes Xiaomi 8 as an example to focus on the stochastic model of smartphones in a multi-system RTK positioning scenario. The research results show that different systems There are significant differences in satellite pseudorange noise, while the difference in phase noise is smaller. Based on this, this paper proposes a stochastic model of base station signal-to-noise ratio combined with comprehensive weighting between satellite systems, which is more efficient than the traditional signal-to-noise ratio model. The weight distribution between different systems was accurately considered. Static and kinematic experiments were conducted under open and occluded conditions. The results show that compared with the traditional signal-to-noise ratio model, the new model is RTK positioning accuracy in the three-dimensional direction under static openness, static occlusion, kinematic openness and kinematic occlusion has increased by 18.7%, 18.3%, 3.3% and 4.6% respectively.
2024, 49(3): 73-79.
doi: 10.12265/j.gnss.2024013
Abstract:
To improve the anti-interference and positioning accuracy of conventional integrated navigation and positioning under the conditions of low-cost satellite receivers and IMU, this paper proposes to fuse GNSS, inertial measurement unit (IMU), and laser radar (LiDAR) to enhance the robustness and accuracy of positioning. In complex environments such as high-rise buildings, where satellite signals are lost, the robustness and accuracy of navigation and positioning can be improved by fusing IMU and GNSS. However, if the satellite signal loss time is too long, the IMU/GNSS integrated positioning accuracy under low-cost conditions is still not ideal. This paper proposes to use the position information output by the LiDAR odometer and the conventional integrated navigation to perform fusion positioning through extended Kalman filter (EKF). The experiments show that in the unobstructed environment, the fusion positioning standard deviation (STD) accuracy is 53.7% higher than the satellite positioning, the root mean square error (RMSE) accuracy is 56% higher, the fusion positioning STD accuracy is 37.9% higher than the GNSS/IMU integrated positioning, and the RMSE accuracy is 38.6% higher. In the obstructed environment, the fusion positioning STD accuracy is 59.4% higher than the satellite positioning, the RMSE accuracy is 71.3% higher, the fusion positioning STD accuracy is 26.3% higher than the GNSS/IMU integrated positioning, and the RMSE accuracy is 33.7% higher.
To improve the anti-interference and positioning accuracy of conventional integrated navigation and positioning under the conditions of low-cost satellite receivers and IMU, this paper proposes to fuse GNSS, inertial measurement unit (IMU), and laser radar (LiDAR) to enhance the robustness and accuracy of positioning. In complex environments such as high-rise buildings, where satellite signals are lost, the robustness and accuracy of navigation and positioning can be improved by fusing IMU and GNSS. However, if the satellite signal loss time is too long, the IMU/GNSS integrated positioning accuracy under low-cost conditions is still not ideal. This paper proposes to use the position information output by the LiDAR odometer and the conventional integrated navigation to perform fusion positioning through extended Kalman filter (EKF). The experiments show that in the unobstructed environment, the fusion positioning standard deviation (STD) accuracy is 53.7% higher than the satellite positioning, the root mean square error (RMSE) accuracy is 56% higher, the fusion positioning STD accuracy is 37.9% higher than the GNSS/IMU integrated positioning, and the RMSE accuracy is 38.6% higher. In the obstructed environment, the fusion positioning STD accuracy is 59.4% higher than the satellite positioning, the RMSE accuracy is 71.3% higher, the fusion positioning STD accuracy is 26.3% higher than the GNSS/IMU integrated positioning, and the RMSE accuracy is 33.7% higher.
2024, 49(3): 80-86.
doi: 10.12265/j.gnss.2023191
Abstract:
Modernized navigation messages of Global Navigation Satellite Systems like BDS, GPS, QZSS and IRNSS include earth rotation parameters (ERPs), namely the pole coordinates and UT－UTC (ΔUT1). Broadcast ERPs are primarily needed for space-borne GNSS applications that require transformations between earth-fixed and inertial reference frames like satellite precise orbit determination as well as earth to the moon navigation. Based on the International GNSS Service (IGS), we obtained broadcast ERP data for BDS, GPS, QZSS, and IRNSS from January 1, 2022 to June 5, 2023. Evaluate the discontinuity of broadcast ERP, the results show that for GPS and IRNSS, the discontinuity of polar motion is 0.9 to 1.4 mas, and the ∆UT1 discontinuity is about 0.2 ms. The discontinuity of polar motion is about 0.1 mas, and the ∆UT1 discontinuity is about 0.02 ms for QZSS. The discontinuity of BDS polar motion is about 4.5 mas, and the ∆UT1 discontinuity is about 1.3 ms. Compared with the external reference 14C04 sequence of the International Earth Rotation and Reference System Service (IERS), the QZSS polar motion error is about 0.6 mas, and the ∆UT1 error is about 0.27 ms. The polar motion of GPS and IRNSS is about 2.4 mas. For ∆UT1, the GPS error is about 0.36 ms, the IRNSS is about 10.47 ms, which is a significant difference. BDS polar motion error is about 6 mas, and ∆UT1 error is about 1.2 ms. Due to less frequent update intervals, compared with GPS, BDS performs worse by a factor of 1.5 and 2.5 for polar motion and ∆UT1 error, respectively.
Modernized navigation messages of Global Navigation Satellite Systems like BDS, GPS, QZSS and IRNSS include earth rotation parameters (ERPs), namely the pole coordinates and UT－UTC (ΔUT1). Broadcast ERPs are primarily needed for space-borne GNSS applications that require transformations between earth-fixed and inertial reference frames like satellite precise orbit determination as well as earth to the moon navigation. Based on the International GNSS Service (IGS), we obtained broadcast ERP data for BDS, GPS, QZSS, and IRNSS from January 1, 2022 to June 5, 2023. Evaluate the discontinuity of broadcast ERP, the results show that for GPS and IRNSS, the discontinuity of polar motion is 0.9 to 1.4 mas, and the ∆UT1 discontinuity is about 0.2 ms. The discontinuity of polar motion is about 0.1 mas, and the ∆UT1 discontinuity is about 0.02 ms for QZSS. The discontinuity of BDS polar motion is about 4.5 mas, and the ∆UT1 discontinuity is about 1.3 ms. Compared with the external reference 14C04 sequence of the International Earth Rotation and Reference System Service (IERS), the QZSS polar motion error is about 0.6 mas, and the ∆UT1 error is about 0.27 ms. The polar motion of GPS and IRNSS is about 2.4 mas. For ∆UT1, the GPS error is about 0.36 ms, the IRNSS is about 10.47 ms, which is a significant difference. BDS polar motion error is about 6 mas, and ∆UT1 error is about 1.2 ms. Due to less frequent update intervals, compared with GPS, BDS performs worse by a factor of 1.5 and 2.5 for polar motion and ∆UT1 error, respectively.
2024, 49(3): 87-93.
doi: 10.12265/j.gnss.2023221
Abstract:
Tree height is a crucial parameter for monitoring forest conditions and photogrammetry stands out as an essential method for tree height acquisition due to its low cost and flexibility. As a passive remote sensing approach, the traditional photogrammetric method often requires a substantial quantity of images with high overlap, which is associated with the sparsity of traditional image features. To enhance tree height extraction accuracy under limited image availability, a proposed approach combines sparse feature matching with dense pixel matching, by employing the epipolar constraint to filter outliers, dense and highly accurate matching results are obtained. The three-dimensional reconstruction algorithm is then applied to generate a point cloud representing the forest scene. This method demonstrates the capability to reconstruct the forest scene comprehensively and extract tree heights even with a small number of images. Comparison with results from LiDAR point clouds yields a correlation coefficient of 0.91 and a maximum error of 1.64 meters. Notably, the algorithm requires only a small number of overlapping images, indicating its potential in handling high-resolution satellite imagery.
Tree height is a crucial parameter for monitoring forest conditions and photogrammetry stands out as an essential method for tree height acquisition due to its low cost and flexibility. As a passive remote sensing approach, the traditional photogrammetric method often requires a substantial quantity of images with high overlap, which is associated with the sparsity of traditional image features. To enhance tree height extraction accuracy under limited image availability, a proposed approach combines sparse feature matching with dense pixel matching, by employing the epipolar constraint to filter outliers, dense and highly accurate matching results are obtained. The three-dimensional reconstruction algorithm is then applied to generate a point cloud representing the forest scene. This method demonstrates the capability to reconstruct the forest scene comprehensively and extract tree heights even with a small number of images. Comparison with results from LiDAR point clouds yields a correlation coefficient of 0.91 and a maximum error of 1.64 meters. Notably, the algorithm requires only a small number of overlapping images, indicating its potential in handling high-resolution satellite imagery.
2024, 49(3): 94-100.
doi: 10.12265/j.gnss.2024015
Abstract:
Aiming at the problem that the localization accuracy and robustness of visual localization systems are easily affected by dynamic feature points in dynamic environments, a dynamic feature point removal method combining target detection and depth information is proposed. The YOLOv7 target detection network is introduced to quickly obtain the target category and position information of the current image frame, and the coordinate attention (CA) mechanism is added to optimize the deep learning model and improve the target detection accuracy of the network. In addition, a dynamic feature point optimization strategy using depth information and pairwise geometric constraints is proposed. Dynamic feature points are effectively eliminated while as many static points as possible are retained, thus reducing the impact of dynamic points on the localization accuracy and robustness of the system. Experimental validation is performed on the publicly available dataset TUM. The results show that the proposed scheme has obvious advantages in terms of localization accuracy and robustness compared with ORB-SLAM2. At the same time, compared with DynaSLAM, the localization accuracy is basically the same, but the operation speed is significantly improved.
Aiming at the problem that the localization accuracy and robustness of visual localization systems are easily affected by dynamic feature points in dynamic environments, a dynamic feature point removal method combining target detection and depth information is proposed. The YOLOv7 target detection network is introduced to quickly obtain the target category and position information of the current image frame, and the coordinate attention (CA) mechanism is added to optimize the deep learning model and improve the target detection accuracy of the network. In addition, a dynamic feature point optimization strategy using depth information and pairwise geometric constraints is proposed. Dynamic feature points are effectively eliminated while as many static points as possible are retained, thus reducing the impact of dynamic points on the localization accuracy and robustness of the system. Experimental validation is performed on the publicly available dataset TUM. The results show that the proposed scheme has obvious advantages in terms of localization accuracy and robustness compared with ORB-SLAM2. At the same time, compared with DynaSLAM, the localization accuracy is basically the same, but the operation speed is significantly improved.
2024, 49(3): 101-106.
doi: 10.12265/j.gnss.2024009
Abstract:
Based on satellite navigation, spatio-temporal entities, web geographic information system (WebGIS), spatial databases, etc., key information on ship attributes, real-time positions, historical paths, associated relationships, and operating statuses has been extracted, leading to the construction of ship entity resources and the establishment of a ship trajectory analysis system. The system provides functions such as map services, trajectory data management, trajectory visual query display, trajectory spatio-temporal analysis display and analysis model parameter configuration. It enables management and analysis of multi-source, multi-temporal and diversified ship trajectory data, presenting ship element characteristics and analysis results, increasing information value density, and enhancing query and analytic abilities. Thus, it enhances the efficiency and accuracy of ship trajectory analysis, serving as a more effective support for ship trajectory analysis business and related applications.
Based on satellite navigation, spatio-temporal entities, web geographic information system (WebGIS), spatial databases, etc., key information on ship attributes, real-time positions, historical paths, associated relationships, and operating statuses has been extracted, leading to the construction of ship entity resources and the establishment of a ship trajectory analysis system. The system provides functions such as map services, trajectory data management, trajectory visual query display, trajectory spatio-temporal analysis display and analysis model parameter configuration. It enables management and analysis of multi-source, multi-temporal and diversified ship trajectory data, presenting ship element characteristics and analysis results, increasing information value density, and enhancing query and analytic abilities. Thus, it enhances the efficiency and accuracy of ship trajectory analysis, serving as a more effective support for ship trajectory analysis business and related applications.
2024, 49(3): 107-114.
doi: 10.12265/j.gnss.2024004
Abstract:
In this paper, we present an evaluation of the water vapor tomography results from four systems-GPS, BDS, GLONASS, and Galileo in terms of accuracy, using the proposed water vapor tomography profile evaluation index TPFS. The results show that the differences in the water vapor tomography solving results of each GNSS are negligible, with the maximum RMSE difference being within 11%. Among these, BDS performs the best in water vapor tomography, while GLONASS performs the worst. Compared with GPS, GLONASS, and Galileo, BDS has a significant advantage in the lower layer (below 2406m). In particular, in the bottom layer, BDS shows a respective improvement of 3.2%, 16.2%, and 5.2% in RMSE compared to GPS, GLONASS, and Galileo. Furthermore, in the comparison of TPFS of tomography water vapor profiles, BDS has the smallest average TPFS and the lowest TPFS of water vapor profiles under heavy rainfall, which is improved by 25.2%, 31.5%, and 32.8% compared to GPS, GLONASS, and Galileo.
In this paper, we present an evaluation of the water vapor tomography results from four systems-GPS, BDS, GLONASS, and Galileo in terms of accuracy, using the proposed water vapor tomography profile evaluation index TPFS. The results show that the differences in the water vapor tomography solving results of each GNSS are negligible, with the maximum RMSE difference being within 11%. Among these, BDS performs the best in water vapor tomography, while GLONASS performs the worst. Compared with GPS, GLONASS, and Galileo, BDS has a significant advantage in the lower layer (below 2406m). In particular, in the bottom layer, BDS shows a respective improvement of 3.2%, 16.2%, and 5.2% in RMSE compared to GPS, GLONASS, and Galileo. Furthermore, in the comparison of TPFS of tomography water vapor profiles, BDS has the smallest average TPFS and the lowest TPFS of water vapor profiles under heavy rainfall, which is improved by 25.2%, 31.5%, and 32.8% compared to GPS, GLONASS, and Galileo.
2024, 49(3): 115-122.
doi: 10.12265/j.gnss.2024011
Abstract:
GNSS data preprocessing plays a crucial role in achieving high-precision navigation, positioning, and attitude applications. However, existing GNSS preprocessing tools and modules face challenges in handling multi-frequency and multi-GNSS observation data. To address this issue, we have developed a GNSS data preprocessing software called (GDPS). GDPS includes modules for data download, format conversion, data editing, quality check and auxiliary tools. The software has been implemented using the PYQT5 tool, which enables a graphical user interface. Experimental tests have demonstrated that the software offers a clear interface, strong interactivity, stable module operation, and comprehensive software functions. Consequently, it effectively caters to the diverse needs of users for preprocessing multi-frequency and multi-GNSS observation data.
GNSS data preprocessing plays a crucial role in achieving high-precision navigation, positioning, and attitude applications. However, existing GNSS preprocessing tools and modules face challenges in handling multi-frequency and multi-GNSS observation data. To address this issue, we have developed a GNSS data preprocessing software called (GDPS). GDPS includes modules for data download, format conversion, data editing, quality check and auxiliary tools. The software has been implemented using the PYQT5 tool, which enables a graphical user interface. Experimental tests have demonstrated that the software offers a clear interface, strong interactivity, stable module operation, and comprehensive software functions. Consequently, it effectively caters to the diverse needs of users for preprocessing multi-frequency and multi-GNSS observation data.
2019, 44(2): 1-12.
doi: DOI:10.13442/j.gnss.1008-9268.2019.02.001
摘要：
2018, 43(1): 43-48.
doi: 0.13442/j.gnss.1008-9268.2018.01.008
摘要：
2020, 45(1): 19-25.
doi: DOI:10.13442/j.gnss.1008-9268.2020.01.003
摘要：
2018, 43(6): 1-7.
doi: doi:10.13442/j.gnss.1008-9268.2018.06.001
摘要：
2018, 43(3): 39-44.
doi: 10.13442/j.gnss.1008-9268.2018.03.007
摘要：
2017, 42(1): 70-73.
doi: 10.13442/j.gnss.1008-9268.2017.01.014
摘要：
2018, 43(1): 19-24.
doi: doi:10.13442/j.gnss.1008-9268.2018.01.004
摘要：
2018, 43(5): 77-83.
doi: 10.13442/j.gnss.1008-9268.2018.05.015
摘要：
2019, 44(2): 1-12.
doi: DOI:10.13442/j.gnss.1008-9268.2019.02.001
Abstract:
2018, 43(6): 8-13.
doi: doi:10.13442/j.gnss.1008-9268.2018.06.002
Abstract:
2019, 44(5): 1-9.
doi: DOI:10.13442/j.gnss.1008-9268.2019.05.001
Abstract:
2019, 44(1): 1-9.
doi: DOI:10.13442/j.gnss.1008-9268.2019.01.001
Abstract:
2017, 42(5): 53-58.
doi: 10.13442/j.gnss.1008-9268.2017.05.011
Abstract:
Bimonthly, Established 1976
Sponsored by：China Institute of Radio Transmission
Competent Authorities：China Electronics Technology Group Corporation
ISSN 1008-9268
CN 41-1317/TN
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