Distributed Orbit Modeling for Predicted GPS Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current GPS systems in mobile devices face challenges in maintaining accurate satellite orbit predictions due to limited computing power and harsh signal environments, leading to increased connectivity demands and latency in data transmission, which affects Time To First Fix (TTFF) and sensitivity.
Innovation Solution
A distributed method for modeling and propagating satellite orbits using a Predicted GPS (PGPS) Server and Client, where the Client generates predicted Orbital State Vectors by propagating initial satellite positions and velocities using force model parameters, reducing the need for real-time network connections and minimizing data transmission volume.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional GPS receivers continuously demodulate broadcast ephemeris data, then position accuracy can be maintained, but network connectivity demands increase and Time To First Fix deteriorates in harsh signal environments
Solution Approach 1:
The system performs preliminary orbit propagation computations on a centralized server to generate predicted satellite positions and velocities extending days into the future. These pre-computed predictions are then transmitted to mobile devices, eliminating the need for continuous demodulation and enabling rapid Time To First Fix while maintaining position accuracy through the use of high-fidelity force models in the preliminary computation phase
Solution Approach 2:
A centralized predicted GPS server acts as an intermediary between satellite orbit data and mobile receivers. The server performs complex orbit propagation using detailed force models and delivers processed prediction results to clients, reducing the computational burden on mobile devices and enabling accurate position solutions without continuous network connectivity or signal demodulation
2Reliability
If GPS receivers in mobile devices attempt to acquire satellite signals in weak signal environments, then position solutions can be obtained, but the computing power requirements and network data transmission demands increase
Solution Approach 1:
The system extracts and separates the computationally intensive orbit propagation functions from mobile devices and relocates them to a centralized server with superior computing resources. The server performs high-fidelity orbit predictions using complex force models, then delivers simplified prediction results to mobile devices, reducing device complexity while maintaining position solution reliability in weak signal environments
Solution Approach 2:
Complex orbit propagation computations are performed in advance on a centralized server using high-fidelity force models. The pre-computed predictions are transmitted to mobile devices, enabling them to achieve reliable position solutions in weak signal environments without requiring equivalent computing power, thereby reducing device complexity while maintaining reliability
3Loss of time
If satellite orbit predictions are extended beyond the standard 4-6 hour ephemeris validity period, then Time To First Fix is improved, but prediction accuracy degrades asymptotically
Solution Approach 1:
The system changes the parameters used in orbit propagation by incorporating high-fidelity force models that account for gravitational perturbations, solar radiation pressure, and other environmental factors. These enhanced parameters enable accurate orbit predictions to be extended from the standard 4-6 hour ephemeris validity period to multiple days, simultaneously improving Time To First Fix and maintaining orbit prediction accuracy through superior modeling
4Ease of operation
If centralized servers provide pre-computed orbit predictions to multiple clients, then network data transmission volume increases, but individual client computing requirements decrease
Solution Approach 1:
The system segments the service architecture into a centralized prediction server that performs computationally intensive orbit propagation and multiple client devices that receive and use the predictions. The server divides the prediction data into manageable segments for transmission to different clients, reducing individual client computing requirements while optimizing network data transmission volume through efficient data segmentation and distribution
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A distributed orbit and propagation method for use in a predicted GPS or GNSS system, which includes a predicted GPS server (PGPS Server), a source of high accuracy orbit predictions (Orbit Server), a global reference network (GRN Server) providing real-time GPS or GNSS assistance data to the PGPS Server, a predicted GPS client (PGPS Client) running on a device equipped with a GPS or AGPS chipset. In response to requests from the PGPS Client, the PGPS Server produces and disseminates an initial seed dataset consisting of current satellite orbit state vectors and orbit propagation model coefficients. This seed dataset enables the PGPS Client to locally predict and propagate satellite orbits to a desired future time. This predictive assistance in turn helps accelerate Time To First Fix (TTFF), optimize position solution calculations and improve the sensitivity of the GPS chip present on, or coupled with, the device. In contrast with other conventional predicted GPS systems that forward large volumes of predicted orbits, synthetic ephemeris or synthetic almanac data, this method optimally reduces data transfer requirements to the client, and enables the client to locally synthesize its own predicted assistance data as needed. This method also supports seamless notification of real-time satellite integrity events and seamless integration of predicted assistance data with industry standard real-time assistance data.