Partial Search Integer Ambiguity Resolution in GPS RTK
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Solution Overview
Problem
Current GPS RTK systems face challenges in resolving integer ambiguities, especially in long-range applications and challenging environments, due to issues like multipath errors and unmodeled systematic biases, which can lead to loss of ambiguity values and prolonged re-initialization times.
Innovation Solution
A partial search process is employed to quickly resolve integer ambiguities using a modified LAMBDA method, which involves identifying a set of satellites, estimating integer ambiguities, and performing a discrimination test to reduce the search space, thereby minimizing computation power requirements and enhancing ambiguity resolution rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a full search process is used to resolve integer ambiguities, then reliability of ambiguity resolution is improved, but computation time and processing power requirements increase significantly
Solution Approach 1:
The patent segments the full search space into multiple partial search spaces based on candidate ambiguity values. Instead of searching all possible integer ambiguity combinations, the method divides the search into manageable parts by identifying candidate values and searching only within defined ranges around these candidates, thereby reducing computational burden while maintaining resolution reliability.
Solution Approach 2:
The patent applies partial action by performing searches only in specific partial regions of the ambiguity space rather than the complete space. The method identifies candidate ambiguity values and performs targeted searches around these candidates, executing partial searches that are sufficient to resolve ambiguities without the excessive computation required for exhaustive full-space searches.
2Area of stationary object
If the receiver separation distance increases, then coverage area is improved, but ambiguity resolution reliability deteriorates due to distance-dependent biases
Solution Approach 1:
The patent performs preliminary identification of candidate ambiguity values and defines partial search spaces before executing the actual search. By pre-processing the ambiguity resolution problem to identify promising candidate regions, the method prepares the search in advance, allowing reliable resolution even over long distances where biases are significant, without requiring exhaustive searches that would be computationally prohibitive.
Solution Approach 2:
The patent changes the search parameters by transitioning from a full-space search approach to a partial-space search approach defined by candidate ambiguity values. This parameter change in the search methodology allows the system to maintain reliability over increased receiver separation distances by focusing computational resources on the most promising ambiguity candidates rather than uniformly searching all possibilities.
3Measurement precision
If multipath errors and systematic biases are present, then measurement accuracy deteriorates, but the patent enables ambiguity resolution without substantial computational resources
Solution Approach 1:
The patent extracts and removes problematic ambiguities from the search set by identifying candidate ambiguity values and defining partial search spaces that exclude regions with significant multipath errors and systematic biases. By taking out the problematic portions of the search space and focusing only on regions with candidate values, the method achieves accurate ambiguity resolution without requiring substantial computational resources to handle all possibilities.
Data Source
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Figure 3A
AI summary
A method for performing integer ambiguity resolution in a global navigation satellite system is disclosed. A set of ambi gui ti es, which are associated wi th carri er phase measurements of at least some of the signals received from the satellites in an identified set of satellites, are identified (block 420 in Figure4A). I nteger ambiguities are estimated and a best candidate set and a second best candidate set of integer ambiguity values are determined (block 430). Upon determining that the best set of integerambiguity val ues f ai I to meet a discrimination test (block 440), each ambiguity for which integerambiguity values i n the best candi date set and second best candi date set fa I to meet predef i ned cri teria are removed from the set of ambiguities to produce a reduced set of ambiguities (block 450 in Figure 4B). The integer ambiguities in the reduced set of ambiguities are then resolved (block 468 in Figure 4C) and an output is generated in accordance with the resolved integerambiguities (block 470 in Figure 4C).