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Objects: It is tried to understand the adjustment from an information theoretic viewpoint. Methods: Besides the concepts of covariance matrix, cofactor matrix and weight matrix, which are often introduced in “Surveying Adjustment” courses, we introduce the concept of Fisher information matrix (or simply information matrix) in this paper. Results: Several important properties of the information matrix are shown; the population and the sample information matrices are distinguished. The information matrix under the assumption of multivariate normal distribution is derived; the links among the population/sample information matrix, the population/sample covariance matrix and the cofactor/weight matrix are explained; and it is revealed that the weight matrix can be viewed as a normalized information matrix. Based on the information matrix, the concept of information vector is introduced; the adjustment in information domain is derived in which the information matrix and the information vector are calculated rather than the parameter vector and the covariance matrix calculated in standard adjustment. Though equivalent with the standard adjustment in terms of the results, the information-domain adjustment has simpler format and clearer structure; this provides a new perspective for understanding the traditional adjustment; more importantly the information-domain method has special merits in solving under-determined model and also in initializing the recursive adjustment method. Finally, the dynamic adjustment in information domain is derived for filtering a dynamic state space model; it is nothing but the information filter which is equivalent to the standard Kalman filter. Conclusions: Information theory provides new insights for comprehensively and thoroughly understanding the surveying adjustment theory. [ABSTRACT FROM AUTHOR]

Objectives：Global navigation satellite system (GNSS) tomography, characterized by reconstructing the three⁃dimensional distribution of the atmospheric water vapor, has proved its capacity for studying the extreme weather events. The ill⁃conditioned problem resulting from the GNSS acquisition geometry is the critical issue of the GNSS tomography system. Algebraic reconstruction techniques, with the advantages of simple iteration and fast convergence, has been widely applied to the tropospheric tomography. However, the error allocation principle based on the intersection length of GNSS rays with each ray⁃voxel affects the accuracy of tomographic results. An improved adaptive algebraic reconstruction tech⁃ niques are proposed to address this problem.Methods：：In view of the unreasonable error allocation in the traditional algorithm, we suggest a new error allocation principle based on the variation of water vapor den⁃ sity (WVD) in voxels for the adaptive algorithms, in which the product of the intersection length and WVD is considered as a new principle to redistribute the difference between the GNSS slant water vapor (SWV) and the reconstructed SWV. Besides, the weight matrix model of elevation angles is introduced to the im⁃ proved algorithm to optimize the tomographic results.Results：：The new algorithms are tested by measured data from Xuzhou continuously operating reference stations (CORS) network and radiosonde during July 2016. Experimental results reveal that the WVD derived from the adaptive algorithms performs better than that of the general algorithms in root mean squared error (RMSE), standard deviation (STD) and mean ab⁃ solute error (MAE), and the RMSE is decreased by 25.91%, 15.81%, and 24.64% for adaptive algebraic reconstruction technique (AART), adaptive multiplicative algebraic reconstruction technique (AMART) and adaptive simultaneous iterative reconstruction technique (ASIRT) respectively. Under the conditions of light rain, moderate rain and heavy rain, the adaptive algorithms retrieve better water vapor profile than the traditional ones.Conclusions: Overall, the water vapor profile derived from the adaptive algorithms agrees with the radiosonde distribution better than the traditional algorithms, which reveals the advantage of the proposed method on optimizing the tomography solutions. [ABSTRACT FROM AUTHOR]

GPS/SINStightly-coupled integration generally performs better than loosely coupled integration because they can get a navigation solution even if the number of GPS satellites is less than four. In order to improve the performance of GPS/SINS tightly-coupled integration, the measurement model of tightly-coupled GPS/INS based on double-difference pseudo-range/pseudo-range rate was proposed. The model based on double-difference pseudo-range was compared with the one based on both double-difference pseudo-range and double-difference pseudo-range rate The actual test shows that the two models can deal with the tightly-coupled integrated navigation. The accuracy of estimation and the convergent time of velocity and attitude error are improved by introducing double-difference pseudo-range rate. [ABSTRACT FROM AUTHOR]

Objectives: Global navigation satellite system (GNSS) tomography technique has become one of the most potential techniques for retrieving the three ‐dimensional (3D) distribution of water vapor with the advantages of high precision observations, low cost and all ‐weather monitoring. Methods: High horizontal resolution water vapor information provided by remote sensing satellites is introduced. We propose a GNSS water vapor tomography algorithm constrained with high horizontal resolution precipitable water vapor (PWV) data for the first time, which supplements and improves the constraints of existing water vapor tomography algorithms. In the proposed algorithm, firstly, the high horizontal resolution PWV observations are calibrated, and then the PWV constraint equations are constructed based on the densified tomographic voxels. Finally, the PWV constraint equations are included into the GNSS tomography model, which optimizes the constraint conditions and improves the tomographic results. Results: GNSS data and remote sensing water vapor data from FY‐3A satellite over Xuzhou area, China in August 2017 are used to assess the feasibility and accuracy of the proposed algorithm. Taking the high precision radiosonde water vapor profile and 3D water vapor density field from ERA5 as reference values, it can be observed that the proposed algorithm is superior to traditional method in retrieving water vapor profile and 3D water vapor distribution. Three kinds of accuracy indexes of the tomographic results have been significantly improved using the proposed method, with the mean root mean square error (RMSE) decreasing from 2.73 g/m3 to 1.78 g/m³, showing an improvement of 34.80%.Conclusions: This highlights that the improved tomography algorithm has significant potential to reconstruct the accurate and reliable 3D atmospheric water vapor distribution. [ABSTRACT FROM AUTHOR]