Impact of different troposphere models on the real-time multi-GNSS PPP performance

Year 2022, Volume: 12 Issue: 3, 756 - 768, 15.07.2022

Berkay Bahadur

https://doi.org/10.17714/gumusfenbil.1061668

Abstract

With the initialization of IGS (International GNSS Service) real-time service products, real-time Precise Point Positioning (PPP) applications have been a popular topic within the GNSS (Global Navigation Satellite Systems) users. The impact of the troposphere on GNSS signals is one of the most crucial error sources regarding the real-time PPP solution. In the PPP technique, the dry component of tropospheric delay is usually corrected by means of empirical models, while its wet component is estimated as an unknown parameter in the adjustment process. Hence, the troposphere model employed in the PPP solution has a considerable impact on the performance of the obtained solution. Therefore, the main objective of this study is to investigate the impact of different troposphere models on the performance of real-time multi-GNSS PPP. As a part of this study, four different troposphere models, where Saastamoinen and Hopfield models that are most frequently used in GNSS solutions are used together with GPT (Global Pressure and Temperature) 2 and 3 models separately, were constituted. In this context, the observation dataset acquired from a total of 16 different IGS stations over a ten-day period of December 19-28, 2021, were processed utilizing four different troposphere models. In addition to the positioning performance, PPP solutions were also analyzed in terms of zenith total delay (ZTD) estimation. The results show that the best positioning performance can be obtained when the Saastamoinen model is used in combination with GPT3. The three-dimensional positioning accuracy acquired from the corresponding solution is 2.72 cm, which is better than the closest solution by a ratio of 9.2%. Besides, in terms of the ZTD estimation, the best performance is achieved again in the case when the Saastamoinen model is used in combination with GPT3. For the related solution, the accuracy of ZTD estimation is calculated as 1.24 cm and this value indicates a better performance by a ratio of 10.2% compared with the closest solution.

Keywords

Real-time, GNSS, Precise Point Positioning, PPP, Troposphere

Farklı troposfer modellerinin gerçek zamanlı çoklu-GNSS PPP performansına etkisi

Abstract

IGS (International GNSS Service) gerçek zamanlı ürünlerin kullanıma açılmasıyla gerçek zamanlı Hassas Nokta Konumlama (Precise Point Positioning, PPP) uygulamaları GNSS (Global Navigation Satellite Systems) kullanıcıları arasında ilgi çekici bir konu haline gelmiştir. Troposferin GNSS sinyalleri üzerindeki etkisi gerçek zamanlı PPP çözümü açısından en önemli hata kaynaklarından bir tanesidir. PPP tekniğinde troposferik gecikmenin kuru bileşeni genellikle deneysel modeller aracılığıyla düzeltilirken ıslak bileşen tahmin sürecinde bilinmeyen bir parametre olarak kestirilir. Dolayısıyla PPP çözümünde kullanılan troposfer modeli elde edilecek çözüm performansı üzerine önemli bir etkiye sahiptir. Bu nedenle bu çalışmanın temel amacı farklı troposfer modellerinin gerçek zamanlı çoklu-GNSS PPP performansına olan etkisini incelemektir. Çalışma kapsamında GNSS çözümlerinde en sık kullanılan Saastamoinen ve Hopfield modellerinin GPT (Global Pressure and Temperature) 2 ve 3 modelleri ile ayrı ayrı kullanıldığı toplamda dört farklı troposfer modeli oluşturulmuştur. Bu kapsamda 19-28 Aralık 2021 tarihleri arasındaki on günlük dönem için toplamda on altı farklı IGS istasyonundan elde edilen gözlem verisi dört farklı troposfer modeli kullanarak işlenmiştir. PPP çözümleri konum belirleme performansına ek olarak toplam zenit gecikme (zenith total delay, ZTD) kestirimi açısından da analiz edilmiştir. Sonuçlar, en yüksek konum belirleme performansının Saastamoinen modelin GPT3 ile eşlenik kullanıldığı durumda elde edilebileceğini göstermektedir. İlgili çözümden elde edilen üç boyutlu konum doğruluğu 2.72 cm olup en yakın çözüme kıyasla %9.2 oranında daha iyidir. Öte yandan, ZTD kestirimi açısından en iyi performans yine Saastamoinen modelin GPT3 ile eşlenik kullanılması durumunda elde edilmiştir. Bu çözümün ise ZTD kestirim doğruluğu 1.24 cm olarak hesap edilmiştir ve bu değer en yakın çözüme göre %10.2 oranında daha iyi bir performansa işaret etmektedir.

Keywords

Gerçek zamanlı, GNSS, Hassas Nokta Konumlama, PPP, Troposfer

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