Acta Geodaetica et Cartographica Sinica

Periodic Variations of BeiDou Satellite Clock Offsets Derived from Multi-satellite Orbit Determination

ZHOU Peiyuan, DU Lan, LU Yu, FANG Shanchuan, ZHANG Zhongkai, YANG Li

2015, 44(12): 1299-1306. doi:10.11947/j.AGCS.2015.20150183

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Multi-satellite orbit determination based on global tracking network can generate satellite orbit and clock products for BDS at the same time. The errors in the two resulting products, however, are difficult to be decoupled completely. There might be periodic fluctuations existing in the satellite clock offsets. Restricted by current imperfect global tracking network, loses of navigation files and software settings, there exist a lot of data gaps in the BeiDou satellite orbit and clock products. A spectrum analysis method applicable to data with gaps was used, and the main periodic items of BeiDou satellite clock offsets were extracted with it. Two improved clock prediction models augmented with periodic corrections were proposed and the prediction accuracy within 24 hours was evaluated. The tested results with nearly one-year-long data showed that the three main periods in BeiDou GEO and IGSO satellite clock offsets are 12, 24 and 8 hours, respectively, while those for MEOs are 12.91, 6.44 and 24 hours. Compared with the conventional clock model of quadratic polynomial, the improved model can increase the prediction accuracy of BeiDou GEO and IGSO satellite clock offsets by 20 to 40 percent at spans less than 24 hours.

Precise Point Positioning with Multi-constellation Satellite Systems: BeiDou、Galileo、GLONASS、GPS

REN Xiaodong, ZHANG Keke, LI Xingxing, ZHANG Xiaohong

2015, 44(12): 1307-1313. doi:10.11947/j.AGCS.2015.20140568

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With the appearance of China's BeiDou system and EU's Galileo system and the restoration and improvement of Russia's GLONASS navigation system, the single GPS navigation satellite system has been gradually developed into multi-GNSS (multi-constellation global navigation satellite systems). Multi-GNSS with precise positioning will become the development trend of GNSS precise positioning in the future. Using the observation data collected by PPP (precise point positioning), which integrates the four major satellite navigation systems of GPS、 GLONASS、 BeiDou and Galileo, this paper conducts a preliminary study and analyzes the positioning performance of multi-system PPP. According to the experiment, in areas where single system observation geometrical configuration is unsatisfactory, multi-system integration can greatly improve the positioning accuracy and convergence rate of PPP. The convergence rate of multi-constellation PPP is 30% to 50% higher than that of single GPS, and the positioning accuracy can also be improved 10% to 30%, especially in terms of elevation direction. Besides, under observation circumstance where the end elevation angle of satellite is larger than 30 degrees, single GPS fails to carry out continuous positioning due to lack of visible satellites, while multi-constellation PPP can still get positioning results, with relatively high accuracy in horizontal direction especially.

Unscented Kalman Filter Algorithm for WiFi-PDR Integrated Indoor Positioning

CHEN GuoLiang, ZHANG Yanzhe, WANG Yunjia, MENG Xiaolin

2015, 44(12): 1314-1321. doi:10.11947/j.AGCS.2015.20140691

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Indoor positioning still faces lots of fundamental technical problems although it has been widely applied. A novel indoor positioning technology by using the smart phone with the assisting of the widely available and economically signals of WiFi is proposed. It also includes the principles and characteristics in indoor positioning. Firstly, improve the system's accuracy by fusing the WiFi fingerprinting positioning and PDR (ped estrian dead reckoning) positioning with UKF (unscented Kalman filter). Secondly, improve the real-time performance by clustering the WiFi fingerprinting with k-means clustering algorithm. An investigation test was conducted at the indoor environment to learn about its performance on a HUAWEI P6-U06 smart phone. The result shows that compared to the pattern-matching system without clustering, an average reduction of 51% in the time cost can be obtained without degrading the positioning accuracy. When the state of personnel is walking, the average positioning error of WiFi is 7.76 m, the average positioning error of PDR is 4.57 m. After UKF fusing, the system's average positioning error is down to 1.24 m. It shows that the algorithm greatly improves the system's real-time and positioning accuracy.

Dynamic Adaptive Model for Indoor WLAN Localization

WU Dongjin, XIA Linyuan

2015, 44(12): 1322-1330. doi:10.11947/j.AGCS.2015.20130780

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To support robust indoor localization, it is presented that a dynamic adaptive model (DAM) for WLAN (wireless local area network) location fingerprinting which can provide updated radio maps depending on the real time data from several base stations (BS). The model takes the spatial relationships between the BSs and the sample points of the radio map into account that the data of BSs and radio map is respectively used as the inputs and outputs of multilayer neural networks to update radio maps dynamically. In order to catch tempo-spatial environmental changes, the multivariate outlier detection technique is applied to examine the data of BSs. According to the detecting results, a retraining process and an interpolation method considering the floor plan are used to update the functional model and make the model adapt to tempo-spatial environmental changes. The model is evaluated in indoor dynamic environments. Compared to conventional ones, the average location error of the proposed model-based method decreases more than 10% in time-varying environments; and after spatial environmental changes (radio beacons are moved), its average location error increases 10% to 20% which is much lower than 165% increase of others. Moreover, the localization accuracy is around 3 m, holding the original performance. The results prove the adaptation of the proposed model to the tempo-spatial environmental changes. However, compared to conventional location fingerprinting, the model brings a little more computational overhead.

Complex Coherence Estimation Based on Adaptive Refined Lee Filter

LONG Jiangping, DING Xiaoli, WANG Changcheng

2015, 44(12): 1331-1339. doi:10.11947/j.AGCS.2015.20140483

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Polarimetric synthetic aperture radar interferometry (PolInSAR) data procedures and their application are based on the estimation of polarimetric complex coherence, which are influenced by size of windows and filter methods. In this paper, the adaptive refined Lee filter, which based on traditional refined Lee filter, is used to estimate the interferometric coherence. The size of filter window is changed by the correlation coefficient between the central sub window and the neighboring sub window. Correlation coefficient which is larger than the threshold value means to the homogeneous pixels in the selected window, and then boxcar filter is chosen to estimate complex coherence. When maximum of correlation coefficient in difference windows sizes is smaller than the threshold value, the refined Lee filter is used to estimate the complex coherence. The efficiency and advantage of the new algorithm are demonstrated with E-SAR data sets. The results show that the influence of speckle noise and edge information is improved; more accurate complex coherence estimated by selected window size and selected pixels increase the accurate of forest parameters inversion.

Establishment and Optimization of Rigorous Geometric Model of Push-broom Camera Using TDI CCD Arranged in an Alternating Pattern

MENG Weican, ZHU Shulong, CAO Wen, ZHU Yongfeng, GAO Xiang, CAO Fanzhi

2015, 44(12): 1340-1350. doi:10.11947/j.AGCS.2015.20150256

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Push-broom cameras using TDI CCD arranged in an alternating pattern are widely carried by typical high-resolution optical satellites in order to obtain high space resolution and enough strip width. For this kind of cameras, several TDI CCD are arranged in an alternating pattern in two lines on the focal plane and push-broom imaging mode is always adopted. Imaging principle and characteristic of this kind of camera is introduced. Exterior parameters of TDI CCD are modeled together based on their same values in any instant of time and an integrated geometric model is finally established. Error compensation methods are designed to remove exterior error and interior error based on this integrated geometric model. A series of tests are designed to verify models and methods proposed in this paper using original image of TH-1 Satellite HR Camera whose detectors are divided into 8 modules arranged in an alternating pattern. As the results, the imaging geometry of this kind of camera can be rigorously described by this integral geometrical model. The positioning accuracy can be obviously improved by our exterior error compensation method, however, different residual error would be remained for different TDI CCD. The positioning accuracy will not be obviously improved while systematic errors of different TDI CCD can be effectively removed by the interior error compensation method. 2 m positioning accuracy in X, Y and Z directions can be achieved and different systematic errors can be removed when both exterior and interior error were compensated. The same accuracy can be achieved in the other scenes when the calculated inner distortion parameters are adopted.

Real-time Observational Water Level Data Stream Online Filtering Method with Hydrological Changes Semantic Constraints

DING Yulin, ZHU Qing, HE Xiaobo, LIN Hui, DU Zhiqiang, ZHANG Yeting, MIAO Shuangxi, YANG Xiaoxia

2015, 44(12): 1351-1358. doi:10.11947/j.AGCS.2015.20140416

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Irregular environmental changes and occasional instrument malfunctions have made noises and exceptions in observational data prominence. Therefore, before processing real-time water level data online, data cleaning is urgently needed to ensure data quality. Since traditional data filtering methods didn't take the data change pattern into consideration, these methods have encountered some severe problems, including the poor adaptability of filter model, the low estimation precision and prohibitively high calculation cost. To overcome these shortcomings, this paper presents a hydrological change semantics constrained online Kalman filtering method: creating dynamic semantic mapping between real-time data changing pattern and the rules of spatial-temporal hydrological process evolution; implementing the change semantic constrained Kalman filtering method to support the adaptive parameter optimization. Observational water level data streams of different precipitation scenarios are selected for testing. Experimental results prove that by means of this method, more accurate and reliable water level information can be available.

Filtering of Airborne LiDAR Point Cloud Based on Variable Radius Circle and B-spline Fitting

ZHENG Jitao, ZHANG Tao

2015, 44(12): 1359-1366. doi:10.11947/j.AGCS.2015.20140514

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Filtering is the key to acquire digital terrain model from airborne LiDAR point cloud. In this paper, a new LiDAR point cloud filtering method is proposed. First, the scanning lines formed with point sequence are obtained throuth scanning the point cloud along the same direction. Then a circle with the variable radius rolling over the bottom of these scanning lines, the purpose is to acquire the points on the ground surface and delete the points on the objects at the same time. The next step is interval sampling from the scanning lines. On this basis, after fitting terrain surface with uniform B-spline surface, every point is projected to the fitting surface and calculate its height. According to compare the real height and its projection height to judge every point is on the terrain surface or not. The experiments show that filtering precision of the algorithm proposed in this paper is improved 1 to 5 times of the traditional methods, it can be used for the city, mountains and forest, and the time complexity of the algorithm is O(n).

Gaussian Mixture Model with Variable Components for Full Waveform LiDAR Data Decomposition and RJMCMC Algorithm

ZHAO Quanhua, LI Hongying, LI Yu

2015, 44(12): 1367-1377. doi:10.11947/j.AGCS.2015.20140501

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Full waveform LiDAR data record the signal of the backscattered laser pulse. The elevation and the energy information of ground targets can be effectively obtained by decomposition of the full waveform LiDAR data. Therefore, waveform decomposition is the key to full waveform LiDAR data processing. However, in waveform decomposition, determining the number of the components is a focus and difficult problem. To this end, this paper presents a method which can automatically determine the number. First of all, a given full waveform LiDAR data is modeled on the assumption that energy recorded at elevation points satisfy Gaussian mixture distribution. The constraint function is defined to steer the model fitting the waveform. Correspondingly, a probability distribution based on the function is constructed by Gibbs. The Bayesian paradigm is followed to build waveform decomposition model. Then a RJMCMC (reversible jump Markov chain Monte Carlo) scheme is used to simulate the decomposition model, which determines the number of the components and decomposes the waveform into a group of Gaussian distributions. In the RJMCMC algorithm, the move types are designed, including updating parameter vector, splitting or merging Gaussian components, birth or death Gaussian component. The results obtained from the ICESat-GLAS waveform data of different areas show that the proposed algorithm is efficient and promising.

Network Kernel Density Estimation for the Analysis of Facility POI Hotspots

YU Wenhao, AI Tinghua, LIU Pengcheng, HE Yakun

2015, 44(12): 1378-1383. doi:10.11947/j.AGCS.2015.20140538

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The distribution pattern of urban facility POIs (points of interest) usually forms clusters (i.e. "hotspots") in urban geographic space. To detect such type of hotspot, the methods mostly employ spatial density estimation based on Euclidean distance, ignoring the fact that the service function and interrelation of urban feasibilities is carried out on the network path distance, neither than conventional Euclidean distance. By using these methods, it is difficult to exactly and objectively delimitate the shape and the size of hotspot. Therefore, this research adopts the kernel density estimation based on the network distance to compute the density of hotspot and proposes a simple and efficient algorithm. The algorithm extends the 2D dilation operator to the 1D morphological operator, thus computing the density of network unit. Through evaluation experiment, it is suggested that the algorithm is more efficient and scalable than the existing algorithms. Based on the case study on real POI data, the range of hotspot can highlight the spatial characteristic of urban functions along traffic routes, in order to provide valuable spatial knowledge and information services for the applications of region planning, navigation and geographic information inquiring.

Estimation of Large Regional Urban and Rural Population Density Based on the Differences of Population Distribution between Urban and Rural: Take Shandong Province as Example

LU Nan, ZHANG Weiwei, CHEN Lijun, LI Zhilin, CHEN Jun, LI Ran, CHEN Xuehong, ZHANG Yushuo, LIU Jiyu

2015, 44(12): 1384-1391. doi:10.11947/j.AGCS.2015.20150005

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Existing methods for large regional population density estimation, which are mostly concentrated in the kilometer scale and only reflect the macro distribution characteristics of the urban and rural population, are difficult to describe details of urban and rural population spatial distribution accurately. In order to resolve the problem above, an estimation method of large regional urban and rural population density, which is based on the first 30 m global land cover dataset(GlobeLand30) is proposed. Based on the urban and rural area data partitioned from artificial surfaces data in GlobeLand30 datasets, the population density were estimated in urban and rural area respectively. Urban population density was estimated through the correlation between night lighting intensity and population. Through area revise of rural patches by the method of quadrats estimation, the rural population density was estimated. This paper takes Shandong province as a test area. The result shows that the method of urban-rural population density estimation could reflect the heterogeneity and continuity of the population spatial distribution in urban internal well, and express the population spatial distribution in rural area. By comparison with the reference data, the method of this paper is superior to the reference data in describing the spatial extent of residents and expressing the spatial distribution of population. And due to the globality of GlobeLand30 data, it is feasible to extend the method to a wider area.

Underground Pipeline Data Matching Considering Multiple Spatial Similarities

GONG Minxia, YUAN Sai, CHU Zhengwei, ZHANG Shuliang, FANG Caili

2015, 44(12): 1392-1400. doi:10.11947/j.AGCS.2015.20150207

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Integrated and professional underground pipeline data are two forms of pipeline.Integrated underground pipeline data is accurate and general, while professional underground pipeline data expresses and contains detailed attribute information.Taking the data of natural gas pipeline as an example, this paper calculates structural similarity measured by the distribution pattern of pipelines that pipeline-point connects with, semantic similarity presented by the names and attributes of the pipeline-point ontology concept, and shape similarity characterized by the shape of arcs between two pipeline-points. The matching of pipe points is realized with the method of support vector machine classification algorithm and unique-matching principle combined with these spatial similarity. Test results show the matching of pipe points is well solved by the proposed algorithm.

Research on the Theory and Algorithm of Triple-frequency GNSS Precise Positioning

HUANG Lingyong

2015, 44(12): 1401-1401. doi:10.11947/j.AGCS.2015.20150395

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