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Fast and Accurate Direct Position Estimation Using Low-Complexity Correlation and Swarm Intelligence Optimization
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DOI：10.3390/rs17101799.png)
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Abstract
En 中文
Direct Position Estimation (DPE) is an alternative GNSS positioning method that models received satellite signals as a function of the receiver's navigation state, allowing for the direct estimation of position, velocity, and time within the navigation domain. However, existing DPE algorithms face significant challenges due to the non-convex nature of the optimization problem and the large solution space, resulting in high computational complexity. To address these challenges, this paper introduces a framework for searching for navigation solutions in DPE through swarm intelligence algorithms, combined with a low-complexity correlation approach. Furthermore, an adaptive Dung Beetle Optimization (ADBO) algorithm is developed. By leveraging insights from fitness landscape analysis, the ADBO algorithm dynamically adjusts subpopulation proportions and the convergence factor while incorporating hybrid mutation strategies for effective adaptation to various types of optimization problems. Benchmark function tests demonstrate that the ADBO algorithm achieves superior convergence performance compared with other popular swarm intelligence algorithms. Both extensive simulations and real GNSS data experiments further validate that the proposed framework, incorporating the ADBO algorithm, achieves improved positioning accuracy compared to traditional positioning methods while outperforming traditional search algorithms and other swarm intelligence algorithms in both accuracy and computational efficiency.
Keywords:
Global Navigation Satellite System (GNSS)
Direct Position Estimation (DPE)
Dung Beetle Optimization (DBO)
Fitness Landscape Analysis (FLA)
Journal
IF:
4.1
Papers:
7.1K
Citations:
15.1W
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