Bridging Real-Time Precise Point Positioning in Natural Hazard Warning Systems during Outages of MADOCA Corrections

Real-time Precise Point Positioning (RT-PPP) is the primary positioning method used in natural hazard warning systems (NHWS), e.g. for monitoring tsunami and earthquakes. The Japanese Multi-GNSS Advanced Demonstration tool for Orbit and Clock Analysis (MADOCA) is a promising service that enables RT-...

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Main Authors: El-Mowafy, Ahmed, Deo, Manoj
Format: Conference Paper
Published: The Institute of Navigation 2017
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/75703
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author El-Mowafy, Ahmed
Deo, Manoj
author_facet El-Mowafy, Ahmed
Deo, Manoj
author_sort El-Mowafy, Ahmed
building Curtin Institutional Repository
collection Online Access
description Real-time Precise Point Positioning (RT-PPP) is the primary positioning method used in natural hazard warning systems (NHWS), e.g. for monitoring tsunami and earthquakes. The Japanese Multi-GNSS Advanced Demonstration tool for Orbit and Clock Analysis (MADOCA) is a promising service that enables RT-PPP. Currently it includes GPS, GLONASS and QZSS orbits and clock corrections in addition to code biases. However, one concern for continuous hazard monitoring by using RT PPP is the severe decline of positioning accuracy if a discontinuity in receiving these corrections occur, for instance due to a temporary user modem failure. In this paper, we present a method that can sustain RT PPP with 3D accuracy less than 20 cm when such a break takes place. For short outages less than 30 minutes we predict MADOCA orbits using a Holt-Winters’ autoregressive model, and for longer outages up to 2 hrs, the most recent International GNSS Service (IGS) ultra-rapid orbits can be used for GPS observations. Moreover, the clock corrections are predicted as a time series using a joint quadratic polynomial and sinusoidal model. The best regression period to estimate the required model parameters is discussed based on autocorrelation analysis of the corrections. The time lengths of the sinusoidal terms are estimated from analysis of the data in the frequency-domain. The prediction model parameters are estimated sequentially using a sliding time window with short intervals to reduce the computational load. Evaluation of the proposed method is performed at a site resembling a NHWS station and positioning accuracy were compared for the cases when using the original corrections and when using the predicted corrections for 1 hr, assuming that within this period the outage can be fixed. The experimental results proved validity of the presented approach where positioning accuracy of 20 cm was maintained during the prediction period.
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spelling curtin-20.500.11937-757032019-07-01T01:18:27Z Bridging Real-Time Precise Point Positioning in Natural Hazard Warning Systems during Outages of MADOCA Corrections El-Mowafy, Ahmed Deo, Manoj Real-Time Precise Point Positioning, Natural Hazard, MADOCA Real-time Precise Point Positioning (RT-PPP) is the primary positioning method used in natural hazard warning systems (NHWS), e.g. for monitoring tsunami and earthquakes. The Japanese Multi-GNSS Advanced Demonstration tool for Orbit and Clock Analysis (MADOCA) is a promising service that enables RT-PPP. Currently it includes GPS, GLONASS and QZSS orbits and clock corrections in addition to code biases. However, one concern for continuous hazard monitoring by using RT PPP is the severe decline of positioning accuracy if a discontinuity in receiving these corrections occur, for instance due to a temporary user modem failure. In this paper, we present a method that can sustain RT PPP with 3D accuracy less than 20 cm when such a break takes place. For short outages less than 30 minutes we predict MADOCA orbits using a Holt-Winters’ autoregressive model, and for longer outages up to 2 hrs, the most recent International GNSS Service (IGS) ultra-rapid orbits can be used for GPS observations. Moreover, the clock corrections are predicted as a time series using a joint quadratic polynomial and sinusoidal model. The best regression period to estimate the required model parameters is discussed based on autocorrelation analysis of the corrections. The time lengths of the sinusoidal terms are estimated from analysis of the data in the frequency-domain. The prediction model parameters are estimated sequentially using a sliding time window with short intervals to reduce the computational load. Evaluation of the proposed method is performed at a site resembling a NHWS station and positioning accuracy were compared for the cases when using the original corrections and when using the predicted corrections for 1 hr, assuming that within this period the outage can be fixed. The experimental results proved validity of the presented approach where positioning accuracy of 20 cm was maintained during the prediction period. 2017 Conference Paper http://hdl.handle.net/20.500.11937/75703 10.33012/2017.15075 The Institute of Navigation restricted
spellingShingle Real-Time Precise Point Positioning, Natural Hazard, MADOCA
El-Mowafy, Ahmed
Deo, Manoj
Bridging Real-Time Precise Point Positioning in Natural Hazard Warning Systems during Outages of MADOCA Corrections
title Bridging Real-Time Precise Point Positioning in Natural Hazard Warning Systems during Outages of MADOCA Corrections
title_full Bridging Real-Time Precise Point Positioning in Natural Hazard Warning Systems during Outages of MADOCA Corrections
title_fullStr Bridging Real-Time Precise Point Positioning in Natural Hazard Warning Systems during Outages of MADOCA Corrections
title_full_unstemmed Bridging Real-Time Precise Point Positioning in Natural Hazard Warning Systems during Outages of MADOCA Corrections
title_short Bridging Real-Time Precise Point Positioning in Natural Hazard Warning Systems during Outages of MADOCA Corrections
title_sort bridging real-time precise point positioning in natural hazard warning systems during outages of madoca corrections
topic Real-Time Precise Point Positioning, Natural Hazard, MADOCA
url http://hdl.handle.net/20.500.11937/75703