Optimizing the angle of arrival positioning error of a UAV via an improved nutcracker optimization algorithm

To address the issues of insufficient accuracy due to nonlinear error accumulation in traditional drone angle-of-arrival (AOA) positioning and the tendency of existing optimization algorithms to get stuck in local optima, this paper proposes a positioning error optimization method that integrates phased correction with an improved Starling optimization algorithm. By constructing a multi-source error propagation model, we analyze the error propagation characteristics of UAV position, attitude, and pod attitude. A phased optimization framework based on observation sequences is designed to suppress the nonlinear accumulation of errors. Subsequently, by integrating the improved Starling optimization algorithm with cubic chaotic mapping and a spiral search strategy, optimal allocation and compensation of error sources are achieved. Monte Carlo simulations demonstrate that the improved algorithm achieves a 73.29% reduction in positioning error distance compared to AOA positioning accuracy and a 58.12% improvement over the original Starling optimization algorithm. It significantly outperforms other comparison algorithms, proving this method effectively corrects nonlinear perturbations in electro-optical systems and provides a higher-precision solution for passive positioning of UAVs.