- Afrikaans
- Albanian
- Amharic
- Arabic
- Armenian
- Azerbaijani
- Basque
- Belarusian
- Bengali
- Bosnian
- Bulgarian
- Catalan
- Cebuano
- China
- Corsican
- Croatian
- Czech
- Danish
- Dutch
- English
- Esperanto
- Estonian
- Finnish
- French
- Frisian
- Galician
- Georgian
- German
- Greek
- Gujarati
- Haitian Creole
- hausa
- hawaiian
- Hebrew
- Hindi
- Miao
- Hungarian
- Icelandic
- igbo
- Indonesian
- irish
- Italian
- Japanese
- Javanese
- Kannada
- kazakh
- Khmer
- Rwandese
- Korean
- Kurdish
- Kyrgyz
- Lao
- Latin
- Latvian
- Lithuanian
- Luxembourgish
- Macedonian
- Malgashi
- Malay
- Malayalam
- Maltese
- Maori
- Marathi
- Mongolian
- Myanmar
- Nepali
- Norwegian
- Norwegian
- Occitan
- Pashto
- Persian
- Polish
- Portuguese
- Punjabi
- Romanian
- Russian
- Samoan
- Scottish Gaelic
- Serbian
- Sesotho
- Shona
- Sindhi
- Sinhala
- Slovak
- Slovenian
- Somali
- Spanish
- Sundanese
- Swahili
- Swedish
- Tagalog
- Tajik
- Tamil
- Tatar
- Telugu
- Thai
- Turkish
- Turkmen
- Ukrainian
- Urdu
- Uighur
- Uzbek
- Vietnamese
- Welsh
- Bantu
- Yiddish
- Yoruba
- Zulu
Analysis of Interference and Deviation of Doppler Shift on LEO Satellite IoT Positioning
Positioning is one of the core capabilities of LEO satellite IoT, supporting location services for global mobile IoT terminals and fixed monitoring nodes. As an inherent effect induced by the high-speed motion of LEO satellites, Doppler shift has become a key factor degrading positioning accuracy. Since positioning in LEO satellite IoT is partially implemented based on Doppler shift measurement, this effect directly interferes with the fundamental positioning principle, leading to various types of positioning biases whose severity differs significantly across positioning modes and application scenarios. This paper analyzes the interference mechanism of Doppler shift on basic positioning principles, classifies and quantifies positioning biases, and discusses the differentiated impacts under various positioning modes and scenarios.
I. Interference Mechanism of Doppler Shift on Basic Positioning Principles
LEO satellite IoT positioning employs multiple measurements of Doppler shift from ground user signals by satellite-borne receivers, and achieves navigation via iso-Doppler curves formed by the intersection of iso-Doppler surfaces with the Earth’s surface. Doppler shift interferes with this principle through pseudorange measurement, carrier-phase tracking, time synchronization, and positioning geometry, ultimately degrading positioning accuracy.
(1) Introduction of Pseudorange Measurement Error
In satellite navigation, ranging relies on signal propagation time. Doppler shift causes carrier-phase measurement errors, which further deteriorate the accuracy of pseudorange calculation. According to the mathematical model of LEO satellite Doppler positioning, the estimation of user position, satellite velocity, and satellite position is directly related to Doppler offset. Thus, Doppler measurement errors are directly propagated into position solution, becoming a major source of positioning error.
(2) Occurrence of Carrier-Phase Ambiguity
Carrier-phase measurement is critical for high-precision centimeter-level positioning. However, the fast time variation of Doppler shift leads to loss-of-lock in carrier-phase tracking loops and introduces integer ambiguity. In the high-dynamic LEO environment, carrier frequency can vary by tens of kHz within seconds, making conventional phase-locked loops (PLL) unable to maintain stable carrier-phase tracking, which greatly reduces the feasibility of high-precision positioning.
(3) Generation of Time Synchronization Error
Positioning computation depends on precise time synchronization between satellites and ground terminals. Doppler shift impairs clock synchronization accuracy. The high-speed movement of LEO satellites induces rapid changes in signal arrival time, increasing the difficulty of time synchronization. Under a Doppler rate of 0.27 ppm/s, time synchronization error can reach tens of nanoseconds, which is sufficient to cause meter-level positioning bias and become a dominant error source for medium- and low-precision positioning.
(4) Degradation of Geometric Dilution of Precision (GDOP)
GDOP is a key metric evaluating the geometric strength of satellite positioning: a lower value indicates higher accuracy. In LEO satellite positioning, the number of visible satellites is relatively small (typically 1–4), and their fast movement results in drastic geometry variation. Doppler measurement error further degrades GDOP, reduces the rationality of positioning geometry, and impairs final positioning accuracy.
II. Classification and Magnitude Analysis of Positioning Biases
Driven by Doppler shift, positioning biases in LEO satellite Doppler systems can be clearly categorized. Their magnitudes are closely related to orbital altitude, observation duration, constellation configuration, and geographic region. Specific characteristics are derived from measured data and simulation.
(1) Distance-Related Bias
Positioning error strongly depends on the satellite–terminal distance. At 800 km orbital altitude with 8-minute observation, a Doppler measurement error of 0.01 Hz leads to approximately 1000 m of user positioning error. As observation time increases, accuracy improves gradually. For single-satellite Doppler positioning, three-dimensional error converges to about 200 m within 8 minutes, with a post-convergence RMS accuracy of approximately 85 m.
(2) Convergence-Time-Related Bias
LEO satellite Doppler positioning requires a convergence period to reach stable accuracy. During convergence, positioning bias is significantly higher than in steady state. Measured data show that single-satellite Doppler error converges to 200 m after about 7.6–7.7 minutes, with a post-convergence RMS of 83–84 m. Before convergence, positioning bias can reach hundreds or even thousands of meters, directly degrading short-observation positioning performance.
(3) Constellation-Configuration-Related Bias
Multi-satellite constellation systems provide much higher accuracy than single-satellite systems, and constellation completeness directly determines bias magnitude. With a mask angle ≤10° and cumulative observation ≥8 minutes, global constellation Doppler positioning achieves a 3D (1σ) mean accuracy better than 7.8 m, RMS better than 15.9 m, and 95th percentile better than 42.2 m, far exceeding single-satellite performance.
(4) Geographic-Region-Related Bias
Positioning accuracy shows strong regional dependence, mainly due to differences in the number of visible satellites, which directly affects Doppler measurement samples and geometric rationality.
- High-latitude regions: 4–9 visible satellites, with optimal equivalent PDOP and accuracy; mean positioning accuracy better than 1.7 m after 8-minute observation.
- Mid-latitude regions: about 1.9–3.6 visible satellites, mean accuracy better than 4.8 m.
- Low-latitude regions: only 1.3–2.4 visible satellites, mean accuracy better than 6.8 m.
III. Differentiated Impacts Under Various Positioning Modes
LEO satellite IoT supports multiple positioning modes: point positioning, differential positioning, carrier-phase positioning, and multi-constellation fusion positioning. Due to differences in principle, precision, and processing capability, Doppler shift exerts distinct influences in each mode.
(1) Point Positioning Mode
In point positioning, the user determines position using signals from one or more satellites. Doppler shift mainly appears as measurement noise and systematic bias. For static receivers, point positioning accuracy is approximately 22 m, which can be improved to about 10 m via refined Doppler bias modeling. In dynamic scenarios, fast-varying Doppler shift may degrade accuracy to 50–100 m. Terminal motion further increases satellite–ground relative velocity and amplifies Doppler effects.
(2) Differential Positioning Mode
Differential positioning measures Doppler shift at reference stations with known positions and provides correction information for users, which effectively eliminates common-mode errors caused by Doppler shift. Studies show that a dual-differenced Doppler framework can reduce three-dimensional positioning error by about 90% on average. In this mode, Doppler shift is no longer the dominant error source; accuracy is mainly limited by the distance between reference station and user, atmospheric delay differences, and other factors.
(3) Carrier-Phase Positioning Mode
Carrier-phase positioning uses high-resolution carrier-phase measurements to achieve centimeter-level accuracy. However, high dynamics in LEO satellites make stable carrier-phase tracking extremely challenging. In Ka-band systems with Doppler shift up to ±480 kHz, conventional tracking algorithms nearly fail. Special high-dynamic tracking techniques—such as FFT-based frequency offset estimation and adaptive PLL—must be adopted to mitigate Doppler shift and maintain stable carrier-phase tracking.
(4) Multi-Constellation Fusion Positioning Mode
Multi-constellation fusion positioning increases visible satellites and improves geometric diversity by fusing signals from multiple LEO constellations, thus significantly enhancing performance. Studies show that using five satellites achieves 3D error of 13.5 m and horizontal error of 6.1 m, improving by 9.4 m and 0.4 m respectively compared with a six-satellite beacon-based method. In this mode, Doppler measurements from different constellations complement each other, reducing single-constellation error and improving stability and accuracy.
IV. Positioning Accuracy Performance in Typical Application Scenarios
In practical LEO satellite IoT applications, terminal status, positioning timeliness, and accuracy requirements vary, leading to different degrees of Doppler influence and distinct positioning performance. Targeted optimizations should be applied according to scenario requirements.
(1) Fixed Facility Monitoring Scenario
In fixed monitoring scenarios such as weather stations and seismic stations, receivers are stationary, and relative motion only comes from satellites, so Doppler influence is relatively mild. At 800 km altitude with 8-minute observation, positioning accuracy is approximately 1000 m. Extending observation to 15 minutes improves accuracy to about 200 m, fully meeting location identification and data traceability requirements.
(2) Mobile Platform Tracking Scenario
In mobile tracking scenarios such as ships and aircraft, receiver motion increases relative velocity, making Doppler effects more complex. Measured data indicate that at 500 km/h, Doppler shift increases by about 10–20%, reducing positioning accuracy by approximately 30–40%. Real-time motion compensation and trajectory-aided Doppler correction are required to maintain expected accuracy.
(3) Emergency Rescue Positioning Scenario
Emergency rescue demands high positioning timeliness. However, LEO satellite Doppler positioning requires convergence time, and time to first fix can reach minutes, which fails to meet instant response needs. Pre-positioning using historical trajectories and orbital prediction can shorten positioning time but sacrifices some accuracy, requiring trade-off between timeliness and precision.
(4) High-Precision Agricultural Application Scenario
Precision agriculture typically requires sub-meter-level accuracy, which is difficult for standalone LEO Doppler positioning due to Doppler-induced measurement errors. In such scenarios, LEO satellite Doppler positioning must be integrated with global navigation satellite systems or assisted by differential techniques. Combined solutions can push accuracy into the 1–3 m range, satisfying operational requirements.
