Relative Navigation System Using Double-Differenced GPS Carrier Phase
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Solution Overview
Problem
Existing relative navigation systems for unmanned aerial vehicles (UAVs) face challenges in providing accurate, reliable, and continuous positioning in dynamic environments with GPS signal blockage or interference, while also adhering to bandwidth constraints.
Innovation Solution
A precision relative navigation system that fuses GPS and inertial measurement unit (IMU) data using double-differenced carrier phase measurements, incorporates a geometry-free carrier phase smoothing algorithm, and employs a compact relative Kalman filter with the Lambda algorithm for integer ambiguity resolution, along with an integrity monitoring system to reject faulty measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS signal is used for positioning in dynamic environments, then position information can be obtained, but accuracy and reliability deteriorate due to signal blockage or interference
Solution Approach 1:
The patent combines GPS positioning with inertial navigation system (INS) to form an integrated navigation system. The INS provides continuous position, velocity, and attitude information independently of GPS signals, while GPS provides periodic updates to correct INS drift. This merging allows the system to maintain reliable positioning even when GPS signals are blocked or interfered with, as the INS can continue providing navigation data without degradation.
2Measurement precision
If multiple independent Kalman filters and numerical ambiguity estimators are used to improve accuracy, then relative position accuracy is improved, but device complexity increases
Solution Approach 1:
The patent merges the Kalman filter and numerical ambiguity estimator into a single integrated processor that simultaneously performs both functions. Instead of running multiple independent filters and estimators separately, the integrated processor combines the state estimation and ambiguity resolution operations, reducing computational overhead and system complexity while maintaining the accuracy benefits of both approaches.
Solution Approach 2:
The integrated processor is designed to perform multiple functions: it acts as both a Kalman filter for state estimation and a numerical ambiguity estimator for GPS carrier phase ambiguity resolution. This multi-functional design eliminates the need for separate dedicated processors for each function, thereby reducing overall system complexity while preserving measurement precision.
3Reliability
If velocity and acceleration from INS are used to aid GPS signal tracking, then GPS performance is enhanced in heavy jamming and high dynamic environments, but system complexity increases
Solution Approach 1:
The patent merges the INS velocity and acceleration outputs directly into the GPS signal tracking loop as aiding inputs. The INS-provided motion parameters are used to predict Doppler shifts and signal dynamics, enabling the GPS receiver to maintain accurate signal tracking during high-dynamic maneuvers and in jamming environments. This integration is achieved through a unified tracking processor that simultaneously handles GPS signal processing and INS data fusion, minimizing additional complexity.
4Measurement precision
If phase measurements and differential GPS are employed to improve accuracy, then navigation accuracy is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent merges differential GPS (DGPS) corrections and carrier phase measurement processing into the integrated GPS/INS navigation solution. The system uses phase measurements from multiple satellites to compute precise relative positions, while simultaneously applying differential corrections from reference stations. The integrated Kalman filter processes all these measurements together with INS data, resolving ambiguities and computing the final navigation solution in a unified framework that optimizes accuracy while managing computational complexity.
Data Source
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AI summary
A first navigation unit of an apparatus in one example comprises a hybrid global positioning system (GPS) / inertial navigation system (INS) component. The hybrid GPS/INS component provides first GPS information and first INS information for the first navigation unit. The first navigation unit is configured to receive second GPS information and second INS information from a second navigation unit. The second navigation unit comprises a GPS component configured to determine a double-differenced GPS carrier phase measurement through employment of the first GPS information and the second GPS information. The first navigation unit comprises a relative Kalman filter configured to update an INS error estimate for an estimate of a relative position between the first and second navigation units based on the double-differenced GPS carrier phase measurement and the first and second INS information.