미지정수 추정 성능 및 신뢰도 향상을 위한 Z-행렬 탐색 알고리즘 개선 기법
Improved Z-Matrix Search Algorithm for Enhanced Integer Ambiguity Estimation and Fix Reliability in RTK
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초록
Reliable integer ambiguity resolution is essential for high-precision, high-integrity Real Time Kinematic (RTK) positioning. The probability of correct fix (P(CF)) provides a key metric for assessing ambiguity resolution reliability and is directly influenced by the integer decorrelation matrix (Z-matrix) performance. This paper proposes an enhanced Z-matrix search method to improve the reliability of integer ambiguity estimation. The method extends the conventional LAMBDA algorithm by generating multiple Z-matrix candidates through systematic linear transformations and expands the search space to select the most reliable candidate based on P(CF). Simulation results from 24-hour single-epoch RTK show the proposed method improves P(CF) in over 97.5% of epochs, reduces the average 1−P(CF) value by approximately 64.1%, and increases fixed solution availability from 85.7% to 95.3%. Although the validation was performed using single-epoch RTK, the proposed method applies to general RTK architectures, including Kalman filter-based systems, and should contribute to faster convergence and more robust ambiguity resolution.
Abstract
Reliable integer ambiguity resolution is essential for high-precision, high-integrity Real Time Kinematic (RTK) positioning. The probability of correct fix (P(CF)) provides a key metric for assessing ambiguity resolution reliability and is directly influenced by the integer decorrelation matrix (Z-matrix) performance. This paper proposes an enhanced Z-matrix search method to improve the reliability of integer ambiguity estimation. The method extends the conventional LAMBDA algorithm by generating multiple Z-matrix candidates through systematic linear transformations and expands the search space to select the most reliable candidate based on P(CF). Simulation results from 24-hour single-epoch RTK show the proposed method improves P(CF) in over 97.5% of epochs, reduces the average 1−P(CF) value by approximately 64.1%, and increases fixed solution availability from 85.7% to 95.3%. Although the validation was performed using single-epoch RTK, the proposed method applies to general RTK architectures, including Kalman filter-based systems, and should contribute to faster convergence and more robust ambiguity resolution.