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Traditional neural network algorithms are prone to consuming a long time and getting stuck in local optima when artificial intelligence methods are applied to geomagnetic navigation and positioning. To address these issues, a method for geomagnetic positioning of aircraft based on improved gradient-based genetic algorithm optimized extreme learning machine neural network (GGA-ELM) is proposed. The training efficiency is greatly improved based on the optimized ELM network and the risk of falling into local optimum is effectively reduced as well by introducing an elite reverse learning strategy into the traditional genetic algorithm. Some aeromagnetic data measured by drone are used for investigation. The experimental results show that the training time of the GGA-ELM model is significantly reduced compared with the CNN, BiLSTM and LSTM models. In addition, the localization error of the GGA-ELM model is about 4 m, and the localization time is 0.003 s. Compared with the ELM, GA-ELM, CNN, BiLSTM, RBF and LSTM models, based on the GGA-ELM method, the localization accuracy is improved by 86.6%, 115.9%, 417.8%, 187.6%, 216.5%, and 107.5%, respectively. The localization time is reduced up to 0.947 s. From the results, it is clearly seen that the proposed method has better positioning stability and higher accuracy on aircraft localization.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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