GPS Accumulated Delta Range Processing for Navigation
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Solution Overview
Problem
Current GPS navigation technologies face challenges in achieving high accuracy and rapid convergence of navigation solutions, particularly after spacecraft maneuvers, due to limitations in velocity estimation and the reliance on propagation models that are prone to errors.
Innovation Solution
The use of accumulated delta range (ADR) measurements, processed through a mathematical formulation that maps these measurements to instantaneous velocity, allowing for more accurate navigation solutions and reduced convergence times by combining ADR differencing with pseudorange measurements and inertial measurements when available.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pseudorange measurements are processed by a Kalman filter with propagation models to improve navigation accuracy, then the navigation solution accuracy is improved, but the convergence time increases from several hours to a day or more
Solution Approach 1:
The patent applies preliminary action by using accumulated delta range (ADR) measurements to pre-establish accurate velocity estimates before the Kalman filter processing begins. The ADR measurements are processed to compute velocity estimates that are then fed into the Kalman filter, allowing the filter to start with more accurate initial conditions and converge faster without sacrificing the high navigation accuracy that propagation models provide.
2Measurement precision
If the Kalman filter uses propagation models with previous state estimates to improve current state accuracy, then the navigation solution accuracy is improved, but the velocity estimate errors propagate and limit the accuracy
Solution Approach 1:
The patent introduces an intermediary approach by using accumulated delta range (ADR) measurements as a mediator between the raw GPS signals and the Kalman filter processing. The ADR measurements are specifically processed to provide improved velocity estimates that serve as a bridge, reducing the propagation of velocity errors through the Kalman filter while maintaining the benefits of sophisticated state estimation.
3Speed
If ADR measurements are differenced over short time intervals to provide current velocity estimates, then the velocity estimate responsiveness is improved, but the measurement accuracy decreases
Solution Approach 1:
The patent applies parameter changes by transforming the ADR measurement processing approach. Instead of simple differencing over short intervals, the patent uses a mathematical formulation that processes ADR measurements with optimized time intervals and weighting factors. This allows the system to achieve both responsiveness and accuracy by adjusting the processing parameters rather than using fixed short-interval differencing.
Data Source
AI summary
Techniques for GPS navigation used to determine the position and velocity of a moving object. Pseudorange (PR) measurements and accumulated delta range (ADR) measurements are made at the object from received GPS signals. Differences are computed between ADR measurements that are separated by a time interval that is greater than a time interval between consecutive ADR measurements. Navigational parameters (e.g., position, velocity and clock) are estimated from the PR measurements and the ADR differences. The ADR measurement equations set for herein are formulated in a much more accurate way so that the time interval between the ADR measurements used to compute an ADR difference can be much larger than that used for current ADR differencing techniques in GPS navigation applications. Consequently, the ADR differences are more accurate, which translates into a much more accurate navigation solution. In addition, the ADR differencing technique contributes to shorten convergence times of the Kalman filter processing, and thereby improve the accuracy of spacecraft navigation. Techniques are also provided to extend these highly accurate ADR processing algorithms to integrated GPS/IMU navigation applications, where IMU data is used as an accurate propagation model to propagate the state vector.