Session A1: Alternatives, Backups, Complements to GNSS

Integrating LEO Satellite Signal Doppler Measurements and DME/VOR for Enhanced GNSS-Free Positioning and Fault Detection
Jiyu Liu, Kun Fang, Xiaowei Lan, Zhipeng Wang, Beihang University; Kelin Zhong, COMAC Shanghai Aircraft Design and Research Institute
Location: Beacon A

With the rapid development of global civil aviation, air traffic has surged dramatically, placing significant pressure on the safety and efficiency of aviation navigation systems. The Global Navigation Satellite System (GNSS) has become a cornerstone of civil aviation navigation, offering significant advantages such as all-weather capability, global coverage, and increasing reliability. GNSS is gradually replacing ground-based navigation aids like Distance Measuring Equipment (DME) and Very High Frequency Omnidirectional Range (VOR). Due to its extensive application, GNSS has become the preferred system in modern aviation, providing accurate and continuous navigation services across various phases of flight, including en route, terminal, and approach operations. Although GNSS plays a critical role in modern aviation navigation, its signals are susceptible to interference, electromagnetic disruptions, or satellite faults, which can impact reliability. To address the potential unreliability of GNSS, the Federal Aviation Administration (FAA) proposed the Minimum Operational Network (MON) concept. MON relies on traditional ground-based navigation systems, such as DME and VOR, to provide a reliable backup in the event of GNSS failure, ensuring that flights maintain sufficient navigation capabilities for safe and efficient operation.
To address the issue of coverage blind spots in ground-based navigation, Low Earth Orbit (LEO) satellite Doppler navigation technology has garnered increasing research attention. On the one hand, LEO satellites offer higher signal strength on the ground and are less prone to interference compared to medium and high-orbit satellites; on the other hand, unlike ground-based navigation systems, they can overcome terrain obstructions to provide more comprehensive coverage. In recent years, the United States' Starlink constellation has been gradually deployed, and China's LEO satellite constellation is actively preparing for launch. The application of these large-scale LEO constellations will provide seamless global navigation coverage for aircraft. Specifically, in environments where GNSS signals are subject to interference, LEO satellites can offer positioning, navigation, and timing (PNT) services to aircraft via Doppler measurements, thereby enhancing the overall effectiveness of navigation systems. By integrating the strong signal and global coverage advantages of LEO satellites with the high reliability and independence of ground-based systems such as DME and VOR, a seamless backup navigation system—Alternative Position, Navigation, and Timing (APNT)—can be established. This approach significantly improves the robustness and redundancy of the entire navigation system, ensuring that aircraft can maintain sufficient safety and continuity of service even in the event of GNSS failure or limitation.
To achieve multi-sensor Integrated Navigation using LEO satellites and DME/VOR systems, this paper proposes a combined navigation method based on the principles of Doppler positioning and velocity measurement using LEO signals. The method employs an Interactive Multiple Model (IMM) Kalman filtering approach for navigation fusion, along with a Multiple Hypothesis Solution Separation (MHSS) Fault Detection method for navigation integrity monitoring.
The core research content includes the following three parts:
Firstly, this paper designs a Multi-Sensor Integrated Navigation method based on LEO signal Doppler measurements combined with DME/VOR, employing the Interactive Multiple Model Kalman Filter for state estimation. The framework of the method includes several steps: model initialization, state prediction, observation fusion, and state update[1]. During the model initialization phase, a constant velocity model and a turn model are established to accommodate different motion states. Initial weights are assigned to both models based on prior knowledge to ensure they can effectively respond to various motion patterns. Additionally, a model transition probability matrix is configured based on historical data, representing the transition probabilities between the constant velocity and turn models. This initialization allows the system to flexibly switch between models depending on the actual motion state. In the state prediction step, the respective kinematic equations of the constant velocity and turn models are used for state prediction. The constant velocity model utilizes a linear motion equation, while the turn model employs a nonlinear turn motion equation. Both models predict the current state and compute their corresponding prediction covariance matrices. During the observation fusion phase, measurements from DME (distance), VOR (azimuth), and the Doppler shift of LEO signals are integrated. Partial derivatives of position, velocity, and receiver clock error are calculated to form the Jacobian matrix for the observation equations. This linearizes the relationship between the measurements from DME, VOR, and LEO signals and the system state. The Jacobian matrix is then used in a Newton iterative method, which iteratively refines the state estimates to minimize the error between the observations and the predicted state. In the state update step, the state estimates and covariance matrices for each model are further refined based on the observations. The likelihood function of each model is calculated using the predicted results and the observations, allowing the assessment of each model’s suitability under the current conditions. Subsequently, Bayesian methods are applied to compute the posterior probability of each model, dynamically adjusting the model weights based on these probabilities. Finally, the state estimates from both the constant velocity and turn models are fused using weighted averages to obtain the final state estimate.
Secondly, this paper proposes an integrity monitoring method for the integrated navigation of LEO Doppler measurements and DME/VOR, based on the Multiple Hypothesis Solution Separation Fault Detection Method. This approach primarily consists of three key steps: fault mode determination, subset solution computation, and solution separation testing. First, the potential fault modes are identified based on the prior fault probabilities of DME, VOR, and LEO systems, along with detection thresholds. In practical applications, the number of LEO satellites in low Earth orbit is in the tens of thousands, far exceeding the number of GNSS satellites. This results in a significant increase in the number of subsets, which dramatically raises the computational complexity. Since some navigation sources are affected by similar factors, traditional methods that traverse all subsets to identify fault modes in Multiple Hypothesis Solution Separation lead to an overly complex subset structure. To address this, the paper introduces a merging method for the subsets of both LEO satellites and ground-based navigation sources. For LEO satellites, since satellites on the same orbital plane share similar orbital parameters (e.g., right ascension of the ascending node and phase angle) and are subject to similar orbital disturbances, navigation sources from the same orbital plane can be merged into a single subset [2]. As for ground-based navigation sources (DME and VOR), since DME and VOR stations with the same location share geographical coordinates and are subject to similar signal interference under complex terrain and extreme weather conditions, these stations can also be treated as a single subset. In this way, the subsets of ground-based navigation sources are effectively simplified. However, while merging similar navigation sources reduces the number of subsets, permanently excluding such sources could significantly reduce the number of available navigation sources in the system. Therefore, it is necessary to further identify the specific faulty sources, allowing the system to retain functioning navigation sources. The first step is to determine the fault mode and partition the subsets accordingly. The second step involves calculating the positioning solutions, standard deviations, and biases for all subsets. Finally, a chi-square test is performed for fault detection and exclusion, followed by the calculation of the protection level and integrity metrics [3].
Finally, simulations and testing were conducted using a simulator to validate the proposed method. A flight route over western China was designed, where there are gaps in ground-based navigation coverage, and DME distance observations were generated using the VIAVI/Aeroflex IFR6000 transponder tester, VOR observations were produced using the VIAVI/Aeroflex IFR4000 navigation tester, and LEO satellite Doppler shift measurements were generated by the satellite navigation simulator. These data were then used to validate the integrated navigation algorithm and the subset reduction algorithm in the Multiple Hypothesis Solution Separation approach developed in this study. The proposed method not only reduces the number of subsets in solution separation testing, thereby lowering computational complexity, but also extends the coverage of navigation services in western China.
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