GNSS Attitude Determination for Remote Sensing: On the Bounding of the Multivariate Ambiguity Objective Function

2013 ◽  
pp. 503-509 ◽  
Author(s):  
Nandakumaran Nadarajah ◽  
Peter J. G. Teunissen ◽  
Gabriele Giorgi
Keyword(s):  
Remote Sensing ◽  
Objective Function ◽  
Attitude Determination

Multivariate Constrained GNSS Real-time Full Attitude Determination Based on Attitude Domain Search

2018 ◽  
Vol 72 (2) ◽  
pp. 483-502
Author(s):  
Hongtao Wu ◽  
Xiubin Zhao ◽  
Chunlei Pang ◽  
Liang Zhang ◽  
Bo Feng
Keyword(s):  
Objective Function ◽  
Success Rate ◽  
High Efficiency ◽  
Attitude Determination ◽  
A Priori ◽  
Three Dimensional ◽  
Satellite System ◽  
New Method ◽  
Lambda Method ◽  
Attitude Angle

A priori attitude information can improve the success rate and reliability of Global Navigation Satellite System (GNSS) multi-antennae attitude determination. However, a priori attitude information is nonlinear, and integrating a priori information into the objective function rigorously will increase the complexity of an ambiguity domain search, such as the Multivariate Constrained-Least-squares Ambiguity Decorrelation Adjustment (MC-LAMBDA) method. In this paper, a new method based on attitude domain search is presented to make use of the a priori attitude angle information with high efficiency. First, the a priori information of pitch and roll is integrated into the search process to derive the analytic search step for attitude angle, and the integer candidates are determined by traversal search in the three-dimensional attitude domain. Then, the objective function is parameterised with Euler angles, and a non-iterative approximate method is utilised to simplify the iterative computation in calculating objective function values. Experimental results reveal that compared to the MC-LAMBDA method, our new method has the same success rate and reliability, but higher efficiency in making use of a priori attitude information.

scholarly journals Impacts of Introducing Remote Sensing Soil Moisture in Calibrating a Distributed Hydrological Model for Streamflow Simulation

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 666 ◽  
Author(s):  
Lihua Xiong ◽  
Ling Zeng
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Objective Function ◽  
Hydrological Model ◽  
Distributed Hydrological Model ◽  
Soil Moisture Data ◽  
Streamflow Data ◽  
The Impact ◽  
Hydrological Variables ◽  
Streamflow Simulation

With the increased availability of remote sensing products, more hydrological variables (e.g., soil moisture and evapotranspiration) other than streamflow data are introduced into the calibration procedure of a hydrological model. However, how the incorporation of these hydrological variables influences the calibration results remains unclear. This study aims to analyze the impact of remote sensing soil moisture data in the joint calibration of a distributed hydrological model. The investigation was carried out in Qujiang and Ganjiang catchments in southern China, where the Dem-based Distributed Rainfall-runoff Model (DDRM) was calibrated under different calibration schemes where the streamflow data and the remote sensing soil moisture are assigned to different weights in the objective function. The remote sensing soil moisture data are from the SMAP L3 soil moisture product. The results show that different weights of soil moisture in the objective function can lead to very slight differences in simulation performance of soil moisture and streamflow. Besides, the joint calibration shows no apparent advantages in terms of streamflow simulation over the traditional calibration using streamflow data only. More studies including various remote sensing soil moisture products are necessary to access their effect on the joint calibration.

scholarly journals Testing a new multivariate GNSS carrier phase attitude determination method for remote sensing platforms

2010 ◽  
Vol 46 (2) ◽  
pp. 118-129 ◽  
Author(s):  
Gabriele Giorgi ◽  
Peter J.G. Teunissen ◽  
Sandra Verhagen ◽  
Peter J. Buist
Keyword(s):  
Remote Sensing ◽  
Carrier Phase ◽  
Attitude Determination ◽  
Determination Method ◽  
Sensing Platforms

Monitoring Temporal Developments from Remote Sensing Data using AI Fine-Grained Segmentation

2021 ◽  
Author(s):  
Samir Zamarialai ◽  
Thijs Perenboom ◽  
Amanda Kruijver ◽  
Zenglin Shi ◽  
Bernard Foing
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Objective Function ◽  
Loss Function ◽  
State Of The Art ◽  
Input Image ◽  
Resolution Image ◽  
Fine Grained ◽  
High Resolution Image ◽  
Resolution Data

<p>Remote sensing (RS) imagery, generated by e.g. cameras on satellites, airplanes and drones, has been used for a variety of applications such as environmental monitoring, detection of craters, monitoring temporal changes on planetary surfaces.</p><p>In recent years, researchers started applying Computer Vision [TP1] methods on RS data. This led to a steady development of remote sensing classification, providing good results on classification and segmentation tasks on RS data.  However, there are still problems with current approaches. Firstly, the main focus is on high-resolution RS imagery. Apart from the fact that these data are not accessible to everyone, the models fail to generalize on lower resolution data. Secondly, the models fail to generalize on more fine-grained classes. For example, models tend to generalize very well on detecting buildings in general, however they fail to distinguish if a building belongs to a fine-grained subclass like residential or commercial buildings. Fine-grained classes often appear very similar to each other, therefore, models have problems to distinguish between them. This problem occurs both in high-resolution and low-resolution RS imagery, however the drop in accuracy is much more significant when using lower resolution data.</p><p>For these reasons, we propose a Multi-Task Convolutional Neural Network (CNN) with three objective functions for segmentation of RS imagery. This model should be able to generalize on different resolutions and receive better accuracy than state-of the-art approaches, especially on fine-grained classes.</p><p>The model consists of two main components. The first component is a CNN that transforms the input image to a segmentation map. This module is optimized with a pixel-wise Cross-Entropy loss function between the segmentation map of the model and the ground truth annotations. If the input image is of lower resolution, this segmentation map will miss out on the complete structure of input images. The second component is another CNN to build a high-resolution image from the low-resolution input image in order to reconstruct fine-grained structure information. This module essentially guides the model to learn more fine-grained feature representations. The transformed image from this module will have much more details like sharper edges and better color. The second CNN module is optimized with a Mean-Squared-Error loss function between the original high-resolution image and the transformed image. Finally, the two images created by the model are then evaluated by a third objective function that aims to learn the distance of similarity between the segmented input image and the super-high resolution segmentation. The final objective function consists of a sum of the three objectives mentioned above. After the model is finished with training, the second module should be detached, meaning high-resolution imagery is only needed during the training phase.</p><p>At the moment we are implementing the model. Afterwards, we will benchmark the model against current state of the art approaches. The status will be presented at EGU 2021.­</p>

Instantaneous Ambiguity Resolved GLONASS FDMA Attitude Determination

2020 ◽  
Author(s):  
Peter Teunissen ◽  
Amir Khodabandeh ◽  
Safoora Zaminpardaz
Keyword(s):  
Remote Sensing ◽  
High Precision ◽  
Ambiguity Resolution ◽  
Attitude Determination ◽  
Direction Finding ◽  
Integer Ambiguity Resolution ◽  
Integer Ambiguity ◽  
Research Centre ◽  
New Model ◽  
Lambda Method

<p><strong>G1 – Geodetic Theory and Algorithms</strong></p><p><strong>G1.3 High-precision GNSS: methods, open problems and Geoscience applications</strong></p><p><strong> </strong></p><p><strong>Instantaneous Ambiguity Resolved GLONASS FDMA Attitude Determination</strong></p><p><strong> </strong></p><p>PJG Teunissen<sup>1,2</sup>, A. Khodabandeh<sup>3</sup>, S. Zaminpardaz<sup>4</sup></p><p><sup>1</sup>GNSS Research Centre, Curtin University, Perth, Australia</p><p><sup>2</sup>Geoscience and Remote Sensing, Delft University of Technology, The Netherlands</p><p><sup>3</sup>University of Melbourne, Melbourne, Australia</p><p><sup>4</sup>RMIT University, Melbourne, Australia</p><p> </p><p>In [1] a new formulation of the double-differenced (DD) GLONASS FDMA model was introduced. It closely resembles that of CDMA-based systems and it guarantees the estimability of the newly defined GLONASS ambiguities. The close resemblance between the new GLONASS FDMA model and the standard CDMA-models implies that available CDMA-based GNSS software is easily modified [2] and that existing methods of integer ambiguity resolution can be directly applied. Due to its general applicability, we believe that the new model opens up a whole variety of carrier-phase based GNSS applications that have hitherto been a challenge for GLONASS ambiguity resolution [3]</p><p>We provide insight into the ambiguity resolution capabilities of the new GLONASS FDMA model, combine it with next-generation GLONASS CDMA signals [4] and demonstrate it for remote sensing platforms that require single-epoch, high-precision direction finding. This demonstration will be done with four different, instantaneous baseline estimators: (a) unconstrained, ambiguity-float baseline, (b) length-constrained, ambiguity-float baseline, (c) unconstrained, ambiguity-fixed baseline, and (d) length-constrained, ambiguity-fixed baseline. The unconstrained solutions are computed with the LAMBDA method, while the constrained ambiguity solutions with the C-LAMBDA method, thereby using the numerically efficient bounding-function formulation of [5]. The results will demonstrate that with the new model, GLONASS-only direction finding is instantaneously possible and that the model and associated method therefore holds great potential for array-based attitude determination and array-based precise point positioning.</p><p> </p><p>[1] P.J.G. Teunissen (2019): A New GLONASS FDMA Model, GPS Solutions, 2019, Art 100.</p><p>[2] A. Khodabandeh and P.J.G. Teunissen (2019): GLONASS-L. MATLAB code archived in GPSTOOLBOX:</p><p>https://www.ngs.noaa.gov/gps-toolbox/GLONASS-L.htm</p><p>[3] R. Langley (2017): GLONASS: Past, present and future. GPS World November 2017, 44-48.</p><p>[4] S. Zaminpardaz, P.J.G. Teunissen and N. Nadarajah (2017): GLONASS CDMA L3 ambiguity resolution</p><p>and positioning, GPS Solutions, 2017, 21(2), 535-549.</p><p>[5] P.J.G. Teunissen PJG (2010): Integer least-squares theory for the GNSS compass. Journal of Geodesy, 84:433–447</p><p> </p><p><strong>Keywords: </strong>GNSS, GLONASS, FDMA, CDMA model, Instantaneous Attitude Determination, Integer Ambiguity Resolution</p>

scholarly journals Weighted objective function selector algorithm for parameter estimation of SVAT models with remote sensing data

2013 ◽  
Vol 49 (10) ◽  
pp. 6959-6978 ◽  
Author(s):  
Joseph A. P. Pollacco ◽  
Binayak P. Mohanty ◽  
Andreas Efstratiadis
Keyword(s):  
Remote Sensing ◽  
Parameter Estimation ◽  
Objective Function ◽  
Remote Sensing Data ◽  
Sensing Data

Observation satellite attitude estimation using sensor measurement and image registration fusion

2017 ◽  
Vol 232 (7) ◽  
pp. 1390-1402 ◽  
Author(s):  
Zhao-Xiang Zhang ◽  
Guo-Dong Xu ◽  
Jia-Ning Song
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Attitude Determination ◽  
Simulated Data ◽  
Star Tracker ◽  
Remote Sensing Images ◽  
Bias Drift ◽  
Satellite Attitude ◽  
Simulation And Experiment ◽  
And Control

In order to enhance the accuracy and the robustness of the attitude determination and control system in observation satellites, a new way to fuse gyro and star tracker measurement with image registration is described. In this method, a novel and complete framework is proposed to estimate the on-orbit attitude variations from multi-spectrum remote sensing images. An extended Kalman filter is derived to calibrate the gyro bias drift and the star tracker error. The new framework is tested with realistically simulated data and remote sensing images based on JL-1 satellite. Simulation and experiment results indicate that based on the image registration, the satellite attitude variations could be detected in real time and applied for the accurate gyro and star tracker bias calibration.

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