Research on Regional GNSS Elevation Anomaly Fitting Method based on IHHO-LSSVM
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摘要: 针对当前复杂区域难以获取较高精度的高程异常值问题，本文提出一种基于IHHO-LSSVM的高程异常拟合方法。首先，采用具有非线性的收敛因子、跳跃距离和自适应权重对哈里斯鹰优化算法(Harris Hawk Optimization, HHO)进行改进；然后，利用改进后的HHO算法为最小二乘向量机(Least Squares Support Vector Machine, LSSVM)高程异常拟合模型提供更为精确的正则化参数和核函数；最后，为验证高程异常组合模型在复杂地形中的适应性，以高程异常值的均方根误差作为评判依据，并结合两组不同地形的工程实例数据进行试验。结果表明，在桥梁带状区域和喀斯特面状区域，相比于HHO-LSSVM法和LSSVM法，IHHO-LSSVM拟合模型的外符合精度更高、稳定性更强、适应性更广，其中桥梁带状区域精度达到0.0101m，喀斯特面状区域达到0.0125m，可为GNSS高程异常拟合模型的建立提供一定的参考价值。
Abstract: In order to solve the problem that it is difficult to obtain high-precision elevation outliers in complex areas, this paper proposes an elevation anomaly fitting method based on IHHO-LSSVM. Firstly, the Harris Hawk Optimization algorithm is improved using nonlinear convergence factors, jump distances, and adaptive weights; Then, the improved HHO algorithm is used to provide more accurate regularization parameters and kernel functions for the Least Squares Support Vector Machine elevation anomaly fitting model; Finally, to verify the adaptability of the elevation anomaly combination model in complex terrain, the root mean square error of the elevation anomaly values was used as the evaluation basis, and experiments were conducted using engineering case data from two different terrains. The results show that in the bridge strip area and karst surface area, compared with the HHO-LSSVM method and LSSVM method, the IHHO-LSSVM method has higher external conformity accuracy, stronger stability, and wider adaptability. The accuracy of the bridge strip area reaches 0.0101m, and the karst surface area reaches 0.0125m, which can provide certain reference value for the establishment of GNSS elevation anomaly fitting models. -
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