Research on Acoustic Target Depth Classification Method Based on Matching Field Processing in Shallow Water
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摘要: 针对现有的浅海水声目标深度分类方法存在的频率适用范围有限、信噪比要求较高等问题，在已获得有效的测距结果的前提下，该文提出一种基于新匹配量的浅海水声目标深度分类算法。通过分析模态互相关项的深度分布特征，建立以垂直复声强为匹配量的目标深度分类模型。接收深度不同时，算法虽均使用垂直复声强为匹配量，但实际上影响深度分类效果的模态互相关项是不同的。根据目标深度分类需求的不同，通过指定双矢量传感器的接收深度，实现目标深度分类模型匹配量的优化选取，从而实现目标深度分类算法性能的提升。仿真结果表明：该算法适用于目标频率激发3阶简正波的情况，算法的频率适用范围得以扩展。算法在低信噪比(SNR)情况下(SNR= 0 dB)、复杂海洋波导中可以获得有价值的深度分类结果。Abstract: Considering the problems of the existing acoustic target depth classification methods in shallow water, such as limited frequency range and high signal-to-noise ratio requirements, on the premise of effective ranging results, a novel target depth classification algorithm based on new matching variable is proposed. By analyzing the depth distribution characteristics of the mode cross-correlation items, the target depth classification model is established by using the vertical complex acoustic intensity as matching variable. When the receiving depths are different, although the algorithms all use the vertical complex acoustic intensity as matching variable, the mode cross-correlation items that directly affect the depth classification effect are different. According to the different target depth classification requirements, by specifying receiving depths of dual vector sensors, the matching variable selection of the target depth classification model can be optimized, thereby achieving the improvement of the target depth classification algorithm performance. The simulation results indicate that this method is suitable for targets whose frequency excites three modes, so as to expand frequency range of the algorithm. The algorithm can obtain valuable depth classification results in complex ocean waveguide under low Signal-to-Noise Ratio (SNR= 0 dB).
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图 3 接收深度存在小幅度起伏时，
${\boldsymbol{I}}_{z{\text{A}}}^{\text{S}}({z_0},r)$ 的相关性与其在理想接收状态下的相关性的差异 -
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