Simplified Algorithms of Variance Component Estimation for Static and Kinematic GPS Single Point Positioning

Wang, Jianguo; Gopaul, Nilesh S.; Scherzinger, B.

doi:10.5081/jgps.8.1.43

Cited by 29 publications

(26 citation statements)

References 15 publications

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“…The topics for ongoing research include a tailored data aggregation step to smooth the high frequency trajectory positions to the TLS data rate and an improvement of the prediction method for position data with lower acquisition rates. In order to get appropriate a priori values for the variance factors and to gain a better understanding of the process noise, a variance component estimation in the Kalman filter will be implemented according to Wang et al (2009). The first step is the estimation of a posteriori variance factor for the observations and the process noise.…”

Section: Discussionmentioning

confidence: 99%

Direct geo-referencing of a static terrestrial laser scanner

Paffenholz

Alkhatib

2010

Journal of Applied Geodesy

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Abstract. This paper describes an adaptive extended Kalman filter (AEKF) approach for georeferencing tasks for a multi-sensor system (MSS). The MSS is a sensor fusion of a phase-measuring terrestrial laser scanner (TLS) with navigation sensors such as Global Navigation Satellite System (GNSS) equipment and inclinometers. The position and orientation of the MSS are the main parameters which are constant on a station and will be derived by a Kalman filtering process. Hence, the orientation of a TLS/MSS can be done without any demand for other artificial targets in the scanning area. However, using inclinometer measurements the spatial rotation angles about the X-and Y-axis of the fixed MSS station can be estimated by the AEKF. This makes it possible to determine all six degrees of freedom of the transformation from a sensor-defined to a global coordinate system. The paper gives a detailed discussion of the strategy used for the direct geo-referencing. The AEKF for the transformation parameters estimation is presented with focus on the modelling of the MSS motion. The usefulness of the suggested approach will be demonstrated using practical investigations.

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Section: Discussionmentioning

confidence: 99%

Direct geo-referencing of a static terrestrial laser scanner

Paffenholz

Alkhatib

2010

Journal of Applied Geodesy

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“…According to the sources, the random information in a system can normally be divided into three independent parts: the process noise vector l x , the measurement noise vector l w and the predicted states noise vector l z [29]. They are defined as follows respectively:…”

Section: Adaptive Filter Based On the Vce Methodsmentioning

confidence: 99%

“…Since the matrix will be negative sometimes during the calculation, some improved methods were proposed. Wang et al proposed an adaptive Kalman filter (AKF) based on the VCE method, and verified its effectiveness using the actual experiments [27,29]. The state estimation algorithm based on VCE has many advantages and has been widely used in linear systems, but few researchers have applied VCE to nonlinear systems.…”

Section: Contributionsmentioning

confidence: 99%

Cooperative Localization Algorithm for Multiple Mobile Robot System in Indoor Environment Based on Variance Component Estimation

Sun

Diao²,

Zhang³

et al. 2017

Symmetry

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Abstract:The Multiple Mobile Robot (MMR) cooperative system is becoming a focus of study in various fields due to its advantages, such as high efficiency and good fault tolerance. However, the uncertainty and nonlinearity problems severely limit the cooperative localization accuracy of the MMR system. Thus, to solve the problems mentioned above, this manuscript presents a cooperative localization algorithm for MMR systems based on Cubature Kalman Filter (CKF) and adaptive Variance Component Estimation (VCE) methods. In this novel algorithm, a nonlinear filter named CKF is used to enhance the cooperative localization accuracy and reduce the computational load. On the other hand, the adaptive VCE method is introduced to eliminate the effects of unknown system noise. Furthermore, the performance of the proposed algorithm is compared with that of the cooperative localization algorithm based on normal CKF by utilizing the real experiment data. In addition, the results demonstrate that the proposed algorithm outperforms the CKF cooperative localization algorithm both in accuracy and consistency.

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“…(7) provides an initial state to the MART algorithm to converge, which will produce a more accurate wet refractivity filed. The advantages of this combined reconstruction algorithm have been demonstrated in several studies (Notarpietro et al, 2011;Wen et al, 2008;Xia et al, 2013). In this study, tomographic model is discretized using the method developed in Chen and Liu (2014).…”

Section: Water Vapor Tomography With Multi-source Datamentioning

confidence: 99%

“…The three weighting factors w 1 , w 2 and w 3 in Eq. (7) are determined by using the Helmert variance component estimation method (Kizilsu and Sahin, 2000;Wang et al, 2009). The reasons for categorizing the GPS, WVR, AERONET, synoptic observations and radiosonde data into one group are as follows: (1) water vapor measurements from these techniques are at a similar level.…”

Section: Water Vapor Tomography With Multi-source Datamentioning

confidence: 99%

Assessing the performance of troposphere tomographic modeling using multi-source water vapor data during Hong Kong's rainy season from May to October 2013

Chen

Liu

2016

Atmos. Meas. Tech.

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Abstract. Acquiring accurate atmospheric water vapor spatial information remains one of the most challenging tasks in meteorology. The tomographic technique is a powerful tool for modeling atmospheric water vapor and monitoring the water vapor spatial and temporal distribution/variation information. This paper presents a study on the monitoring of water vapor variations using tomographic techniques based on multi-source water vapor data, including GPS (Global Positioning System), radiosonde, WVR (water vapor radiometer), NWP (numerical weather prediction), AERONET (AErosol RObotic NETwork) sun photometer and synoptic station measurements. An extensive investigation has been carried out using multi-source data collected from May to October 2013 in Hong Kong. With the use of radiosonde observed profiles, five different vertical a priori information schemes were designed and examined. Analysis results revealed that the best vertical constraint is to employ the average radiosonde profiles over the 3 days prior to the tomographic time and that the assimilation of multi-source data can increase the tomography modeling accuracy. Based on the best vertical a priori information scheme, comparisons of slant wet delay (SWD) measurements between GPS data and multi-observational tomography showed that the root mean square error (RMSE) of their differences is 10.85 mm. Multi-observational tomography achieved an accuracy of 7.13 mm km −1 when compared with radiosonde wet refractivity observations. The vertical layer tomographic modeling accuracy was also assessed using radiosonde water vapor profiles. An accuracy of 11.44 mm km −1 at the lowest layer (0-0.4 km) and an RMSE of 3.30 mm km −1 at the uppermost layer (7.5-8.5 km) were yielded. At last, a test of the tomographic modeling in a torrential storm occurring on 21-22 May 2013 in Hong Kong demonstrated that the tomographic modeling is very robust, even during severe precipitation conditions.

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Simplified Algorithms of Variance Component Estimation for Static and Kinematic GPS Single Point Positioning

Cited by 29 publications

References 15 publications

Direct geo-referencing of a static terrestrial laser scanner

Direct geo-referencing of a static terrestrial laser scanner

Cooperative Localization Algorithm for Multiple Mobile Robot System in Indoor Environment Based on Variance Component Estimation

Assessing the performance of troposphere tomographic modeling using multi-source water vapor data during Hong Kong's rainy season from May to October 2013

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