Research on Nonlinear Error Compensation and Intelligent Optimization Method for UAV Target Positioning

TL;DR

This paper introduces a new optimization algorithm to improve the accuracy of UAV target positioning by addressing nonlinear errors and multi-source error coupling.

Contribution

The novel KYCOA algorithm enhances UAV positioning accuracy by combining improved initialization and position updating strategies.

Findings

KYCOA reduces positioning error distance by 66.75% compared to the original COA.

The algorithm shows 41.89% and 62.06% improvement over GWO and WOA, respectively.

Real flight tests confirm a 40% average reduction in localization error compared to other algorithms.

Abstract

The realization of high-precision target positioning requires the systematic suppression of nonlinear perturbations in the UAV optoelectronic system and the optimization of the cumulative deviation of coordinate transformations through error transfer modeling. This study proposes an error allocation method based on the improved raccoon optimization algorithm (KYCOA) to resolve the problem of degradation of positioning accuracy due to multi-source error coupling during UAV target positioning. Firstly, a multi-coordinate system transformation model is established to analyze the nonlinear transfer characteristics of the error, and the Taylor expansion is used to linearize the error transfer process and derive the synthetic error model under the geocentric coordinate system. Secondly, the KYCOA is proposed to optimize the error allocation by combining the good point set initialization strategy to enhance the population diversity, and the golden sine algorithm to improve the position updating mechanism in response to the defect of the traditional optimization algorithm, which easily falls into the local optimum. Simulation experiments show that the positioning error distance of the KYCOA is reduced by 66.75%, 41.89%, and 62.06% when compared with that of the original Coati Optimization Algorithm (COA), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA), respectively. In the real flight test, the target point localization error of the KYCOA is reduced by more than 40% on average when compared with that of other algorithms, which verifies the effectiveness of the proposed method in improving the target localization accuracy and robustness of UAVs.

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