scholarly journals A Unified Framework for GPS Code and Carrier-Phase Multipath Mitigation Using Support Vector Regression

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Quoc-Huy Phan ◽  
Su-Lim Tan ◽  
Ian McLoughlin ◽  
Duc-Lung Vu
Keyword(s):  
Carrier Phase ◽  
Support Vector ◽  
Unified Framework ◽  
Multipath Mitigation ◽  
Regression Problem ◽  
Multipath Error ◽  
Accuracy And Precision ◽  
Kernel Support Vector Machine ◽  
Global Positioning ◽  
Error Standard Deviation

Multipath mitigation is a long-standing problem in global positioning system (GPS) research and is essential for improving the accuracy and precision of positioning solutions. In this work, we consider multipath error estimation as a regression problem and propose a unified framework for both code and carrier-phase multipath mitigation for ground fixed GPS stations. We use the kernel support vector machine to predict multipath errors, since it is known to potentially offer better-performance traditional models, such as neural networks. The predicted multipath error is then used to correct GPS measurements. We empirically show that the proposed method can reduce the code multipath error standard deviation up to 79% on average, which significantly outperforms other approaches in the literature. A comparative analysis of reduction of double-differential carrier-phase multipath error reveals that a 57% reduction is also achieved. Furthermore, by simulation, we also show that this method is robust to coexisting signals of phenomena (e.g., seismic signals) we wish to preserve.

Assessment of wavelets analysis for carrier-phase multipath mitigationThis article is one of a series of papers published in this Special Issue on the theme GEODESY.

2009 ◽  
Vol 46 (8) ◽  
pp. 627-636
Author(s):  
Ahmed A. El-Ghazouly ◽  
Mohamed Elhabiby ◽  
Naser El-Sheimy
Keyword(s):  
Carrier Phase ◽  
Deformation Monitoring ◽  
Double Difference ◽  
Multipath Mitigation ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Adaptive Wavelet ◽  
Multipath Error ◽  
Phase Measurements ◽  
Search Volume

In carrier-phase measurements, which are the most precise observations for Global Positioning System (GPS) relative positioning, multipath error is still a factor that interferes with achieving the desired accuracy. Various improvements in receiver and antenna technologies, as well as modeling strategies, have resulted in better ways of coping with this error source. However, errors caused by multipath can be as large as 5 cm, which is not an acceptable accuracy, especially in precise surveying applications like deformation monitoring. In this paper, a full assessment of different wavelets techniques that can be used in multipath mitigation is made to evaluate the optimum way of using wavelets to reduce or remove this type of error. Also, a new approach based on the wavelet detrending technique is introduced to remove carrier-phase multipath error in the measurement domain. To mitigate multipath, GPS double-difference observables are fed to an adaptive wavelet analysis procedure based on high- and low-pass filter decomposition with different levels of resolution. Consequently, the observable sequences are corrected; these corrected observables can then be used to reduce the ambiguity search volume during the initial float solution stage. Meanwhile, double-difference observations with multipath mitigation offer an efficient method for obtaining a better baseline solution.

GPS Multipath Estimation with Improved SNR Adaptation Performance Utilizing Adaptive Filters

2013 ◽  
Vol 774-776 ◽  
pp. 1664-1670
Author(s):  
Yan Xin Yao ◽  
Yun Zhao ◽  
Jun Ye Zeng
Keyword(s):  
Global Positioning System ◽  
Carrier Phase ◽  
Adaptive Filters ◽  
Sampling Interval ◽  
Direct Path ◽  
Multipath Mitigation ◽  
Global Positioning ◽  
Multipath Signals ◽  
Carrier Phase Ambiguity ◽  
Better Than

Global positioning system multipath estimation is important for both high precision positioning and reflection applications. The paper aims at proposing a method for estimating multipath parameters including carrier phases with good SNR adaptation performance utilizing adaptive filters. The method takes the signal after demodulation and spectrum dispreading for the desired signal. It is found to have a desired signal with relatively higher SNR so that the filtering performance is relatively better than previously proposed method; whats more, the method could estimate multipath code delay preferable to carrier phase ambiguity problem. Simulations validate the method can estimate multipath profile including amplitudes, code delay and carrier phases of direct path and multipath signals with small errors of 2% correct amplitude, smaller than one sampling interval code delay and 0.01 cycles carrier phase at 11dB SNR. The multipath mitigation performance of the method is better than that of narrow correlator.

Fog Enabled Cloud Based Intelligent Resource Management Approach Using Improved Grey Wolf Optimization Strategy and Kernel Support Vector Machine

2021 ◽  
Vol 18 (4) ◽  
pp. 1275-1281
Author(s):  
R. Sudha ◽  
G. Indirani ◽  
S. Selvamuthukumaran
Keyword(s):  
Support Vector Machine ◽  
Resource Management ◽  
Support Vector ◽  
Management Approach ◽  
Optimization Strategy ◽  
Grey Wolf ◽  
Processing Load ◽  
Grey Wolf Optimization ◽  
Kernel Support Vector Machine ◽  
Service Response Time

Resource management is a significant task of scheduling and allocating resources to applications to meet the required Quality of Service (QoS) limitations by the minimization of overhead with an effective resource utilization. This paper presents a Fog-enabled Cloud computing resource management model for smart homes by the Improved Grey Wolf Optimization Strategy. Besides, Kernel Support Vector Machine (KSVM) model is applied for series forecasting of time and also of processing load of a distributed server and determine the proper resources which should be allocated for the optimization of the service response time. The presented IGWO-KSVM model has been simulated under several aspects and the outcome exhibited the outstanding performance of the presented model.

scholarly journals Smarter Robotic Sprayer System for Precision Agriculture

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2061
Author(s):  
André Rodrigues Baltazar ◽  
Filipe Neves dos Santos ◽  
António Paulo Moreira ◽  
António Valente ◽  
José Boaventura Cunha
Keyword(s):  
Precision Agriculture ◽  
Vegetation Index ◽  
Reference Value ◽  
Support Vector ◽  
Svm Classifier ◽  
Accuracy Score ◽  
Accuracy And Precision ◽  
Perception System ◽  
Water Rate ◽  
Leaf Density

The automation of agricultural processes is expected to positively impact the environment by reducing waste and increasing food security, maximising resource use. Precision spraying is a method used to reduce the losses during pesticides application, reducing chemical residues in the soil. In this work, we developed a smart and novel electric sprayer that can be assembled on a robot. The sprayer has a crop perception system that calculates the leaf density based on a support vector machine (SVM) classifier using image histograms (local binary pattern (LBP), vegetation index, average, and hue). This density can then be used as a reference value to feed a controller that determines the air flow, the water rate, and the water density of the sprayer. This perception system was developed and tested with a created dataset available to the scientific community and represents a significant contribution. The results of the leaf density classifier show an accuracy score that varies between 80% and 85%. The conducted tests prove that the solution has the potential to increase the spraying accuracy and precision.

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