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Solving Integer Ambiguity Based on an Improved Ant Lion Algorithm.

Wuzheng Guo1,2, Yuanfa Ji1,2,3, Xiyan Sun1,2,3,4

  • 1Information and Communicaiton School, Guilin University of Electronic Technology, Guilin 541004, China.

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Summary
This summary is machine-generated.

An enhanced Simulated Annealing Ant Lion Optimizer (SAALO) algorithm improves Global Navigation Satellite System (GNSS) integer ambiguity resolution. SAALO demonstrates faster solving speeds and higher success rates compared to existing methods in various scenarios.

Keywords:
centimeter-level accuracy positioningdouble-difference carrier phaseglobal navigation and positioning systemhigher-dimensional ambiguity resolutioninteger ambiguity

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Area of Science:

  • Geomatics Engineering
  • Satellite Navigation Systems
  • Optimization Algorithms

Background:

  • Global Navigation Satellite Systems (GNSS) rely on double-difference carrier phase observations for high-accuracy positioning.
  • Resolving integer ambiguities within the GNSS model is crucial for achieving precise coordinates.
  • Existing algorithms face challenges in efficiently and accurately resolving these ambiguities, especially in high-dimensional scenarios.

Purpose of the Study:

  • To introduce and evaluate an enhanced Simulated Annealing Ant Lion Optimizer (SAALO) for GNSS double-difference integer ambiguity resolution.
  • To assess the performance of SAALO in terms of speed and success rate against established algorithms.
  • To validate SAALO's capability in high-dimensional and real-world short- and medium-baseline GNSS applications.

Main Methods:

  • Development of an enhanced Simulated Annealing Ant Lion Optimizer (SAALO).
  • Comparative analysis of SAALO against Ant Lion Optimization Algorithm (ALO), LAMBDA, and MLAMBDA for solving speed and success rate.
  • High-dimensional integer ambiguity resolution tests in 6- and 12-dimensional scenarios.
  • Practical application testing in short- and medium-baseline scenarios using a single-frequency GPS system.

Main Results:

  • SAALO demonstrated faster solution times than LAMBDA and MLAMBDA by 0.0496 s and 0.01 s, respectively.
  • In high-dimensional tests (6D and 12D), SAALO achieved a success rate exceeding 98%.
  • In real-world GPS tests, SAALO showed a slightly faster average solution time and a 5.2% higher success rate compared to LAMBDA for a 42.7 km baseline.

Conclusions:

  • The enhanced SAALO algorithm is effective and reliable for GNSS integer ambiguity resolution.
  • SAALO offers improved performance in terms of speed and accuracy, particularly in high-dimensional and practical applications.
  • This algorithm presents a robust solution for enhancing the precision of GNSS positioning.