On-the-fly ambiguity resolution method for pseudolite/INS integration based on double-difference square observations

GPS Solutions ◽  
2021 ◽  
Vol 25 (4) ◽  
Author(s):  
Shijie Yun ◽  
Zheng Yao ◽  
Mingquan Lu
Keyword(s):  
Ambiguity Resolution ◽  
Double Difference ◽  
Resolution Method

scholarly journals Sequential Ambiguity Resolution Method for Poorly-Observed GNSS Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2106
Author(s):  
Haiyang Li ◽  
Guigen Nie ◽  
Shuguang Wu ◽  
Yuefan He
Keyword(s):  
Success Rate ◽  
Ambiguity Resolution ◽  
Satellite System ◽  
Integer Ambiguity ◽  
Double Difference ◽  
Resolution Method ◽  
Substitution Process ◽  
Iterative Correction ◽  
Ambiguity Success Rate ◽  
Gnss Data

Integer ambiguity resolution is required to obtain precise coordinates for the global navigation satellite system (GNSS). Poorly observed data cause unfixed integer ambiguity and reduce the coordinate accuracy. Previous studies mostly used denoise filters and partial ambiguity resolution algorithms to address this problem. This study proposes a sequential ambiguity resolution method that includes a float solution substitution process and a double-difference (DD) iterative correction equation process. The float solution substitution process updates the initial float solution, while the DD iterative correction equation process is used to eliminate the residual biases. The satellite-selection experiment shows that the float solution substitution process is adequate to obtain a more accurate float solution. The iteration-correction experiment shows that the double-difference iterative correction equation process is feasible with an improvement in the ambiguity success rate from 28.4% to 96.2%. The superiority experiment shows significant improvement in the ambiguity success rate from 36.1% to 83.6% and a better baseline difference from about 0.1 m to 0.04 m. It is proved that the proposed sequential ambiguity resolution method can significantly optimize the results for poorly-observed GNSS data.

scholarly journals A Multi-GNSS Differential Phase Kinematic Post-Processing Method

2020 ◽  
Vol 12 (17) ◽  
pp. 2727
Author(s):  
Ruijie Xi ◽  
Qusen Chen ◽  
Xiaolin Meng ◽  
Weiping Jiang ◽  
Xiangdong An ◽  
...  
Keyword(s):  
Ambiguity Resolution ◽  
Processing Method ◽  
Vibration Monitoring ◽  
Double Difference ◽  
Standard Normal Distribution ◽  
Post Processing ◽  
Differential Phase ◽  
Short Baselines ◽  
Long Baselines ◽  
Better Than

We propose a multiple global navigation satellite system (multi-GNSS) differential phase kinematic post-processing method, expand the current Track ability, and finely tune processing parameters to achieve the best results for research purposes. The double-difference (DD) phase formulas of GLONASS are especially formulated, and the method of arc ambiguity resolution (AR) in post-processing is developed. To verify the feasibility of this AR method, a group of static baselines with ranges from 8 m to 100 km and two kinematic tests were used. The results imply that 100% of ambiguities in short baselines and over 90% in long baselines can be fixed with the proposed ambiguity resolution method. Better than a 10-mm positioning precision was achieved for all the horizonal components of those selected baselines and the vertical components of the short baselines, and the vertical precision for long baselines is around 20 to 40 mm. In the posterior residual analysis, the means of the residual root-mean-squares (RMSs) of different systems are better than 10 mm for short baselines and at the range of 10–20 mm for baselines longer than 80 km. Mostly, the residuals satisfy the standard normal distribution. It proves that the new method could be applied in bridge displacement and vibration monitoring and for UAV photogrammetry.

scholarly journals A New GNSS Single-Epoch Ambiguity Resolution Method Based on Triple-Frequency Signals

2017 ◽  
Vol 6 (2) ◽  
pp. 46 ◽  
Author(s):  
Shengli Wang ◽  
Jian Deng ◽  
Xiushan Lu ◽  
Ziyuan Song ◽  
Ying Xu
Keyword(s):  
Ambiguity Resolution ◽  
Resolution Method ◽  
Triple Frequency ◽  
Single Epoch

A Novel Ambiguity Resolution Method of Radar Pulses using Genetic Algorithm

2015 ◽  
Vol 52 (4) ◽  
pp. 184-193
Author(s):  
Jinwoo Han ◽  
Jeil Jo ◽  
Sanhae Kim ◽  
Jintae Park ◽  
Kyuha Song
Keyword(s):  
Genetic Algorithm ◽  
Ambiguity Resolution ◽  
Resolution Method

scholarly journals A Micro-Doppler Frequency Ambiguity Resolution Method Based on Complex-Valued U-Net

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ming Long ◽  
Jun Yang ◽  
Saiqiang Xia ◽  
Mingjiu Lv ◽  
Xu Wei ◽  
...  
Keyword(s):  
Ambiguity Resolution ◽  
Pulse Repetition Frequency ◽  
Doppler Frequency ◽  
Sampling Theorem ◽  
Repetition Frequency ◽  
Pulse Repetition ◽  
Resolution Method ◽  
Time Frequency ◽  
Short Time ◽  
Complex Valued

In order to resolute the micro-Doppler frequency ambiguity caused by radar pulse repetition frequency not high enough (i.e., pulse dimension does not satisfy the requirement of Nyquist sampling theorem), this paper presents a micro-Doppler frequency ambiguity resolution method based on complex-valued U-net. The echo sequence is interpolated by zeros in the pulse dimension to increase the equivalent pulse repetition frequency, so that the echo sequence after zero interpolation contains the real micro-Doppler frequency; at the same time, some new frequency components are generated. The variation law of the echo sequence frequency after zero interpolation is analyzed. Then, the echo sequence in time domain after zero interpolation is transformed to the time-frequency domain by short-time Fourier transform (STFT). Finally, the time-frequency results can be segmented by the model, which is trained by complex-valued U-net to eliminate the redundant frequencies generated by zero interpolation; thus, the reconstruction of real micro-Doppler frequency is realized. Theoretical analysis and simulation results show that the proposed method can solve the problem of micro-Doppler frequency ambiguity. Compared with fully convolution network (FCN) and fully convolution residual network (FCRN), the proposed method has better performance and robustness.

scholarly journals Improving the Stochastic Model for VRS Network-Based GNSS Surveying

2019 ◽  
Vol 54 (1) ◽  
pp. 17-30 ◽  
Author(s):  
Thanate Jongrujinan ◽  
Chalermchon Satirapod
Keyword(s):  
Stochastic Model ◽  
Covariance Matrix ◽  
Ambiguity Resolution ◽  
Reference Station ◽  
Double Difference ◽  
Atmospheric Effects ◽  
Equal Weight ◽  
Virtual Reference ◽  
Positioning Accuracy ◽  
Variance Covariance Matrix

Abstract The VRS network-based technique has become the main precise GNSS surveying method especially for medium-range baselines (approximately 20-70 km). The key concept of this approach is to use the observables of multiple reference stations to generate the network correction in the form of a virtual reference station for mitigating distance-dependent errors including atmospheric effects and orbital uncertainty at the user’s location. Numerous GNSS data processing strategies have been adopted in the functional model in order to improve both the positioning accuracy and the success of ambiguity resolution. However, it is impossible to completely model the aforementioned errors. As a result, the unmodelled residuals still remain in the virtual reference station observables when the least squares estimation is employed. An alternative approach to deal with these residuals is to construct a more realistic stochastic model whereby the variance-covariance matrix is assumed to be homoscedastic. This research aims to investigate a suitable stochastic model used for the VRS technique. The rigorous statistical method, MINQUE has been applied to estimate the variance-covariance matrix of the double-difference observables for a virtual reference station to rover baseline determination. The findings of the comparison to the equal-weight model and the satellite elevation-based model indicated that the MINQUE procedure could enhance the positioning accuracy. In addition, the reliability of ambiguity resolution is also improved.

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