Underwater target azimuth estimation is a critical technology in array signal processing, with wide applications in military operations, marine resource development, and environmental monitoring. A comprehensive review of the current development status of underwater target azimuth estimation methods is provided in this paper. Firstly, an introduction to the acoustic mathematical model based on an uniformly distributed sound pressure line array was given. Next, azimuth estimation methods are classified into four categories: classical beamforming, statistical, subspace, and AI-based Direction of Arrival (DOA) estimation methods. Key factors affecting azimuth estimation accuracy, such as array calibration errors, array geometry, signal processing techniques, and underwater acoustic channel characteristics, were also analyzed. Finally, the paper discussed the limitations of current azimuth estimation technologies and proposed future research directions, including multimodal data fusion, integration of deep learning with physical models, and the development of new array structures etc, to enhance the accuracy and robustness of underwater azimuth estimation.
| 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 |