The physical mechanism and signal characteristics of acoustic scattering are the vital basis for target recognition. But underwater target acoustic scattering components are aliasing in time-frequency (TF) domain, for which the target elastic acoustic scattering characteristics are difficult to detect. Additionally, the existing blind source separation methods are effective only on condition that the number of array elements is equal to or greater than the number of the source signals. To address these problems, a novel TF domain blind source extraction method of separating target acoustic scattering components is proposed in this paper. The method only uses the TF energy characteristic differences among the target acoustic scattering components, and special limitations on target echo structures are unnecessary. Image morphology filter is used to remove the cross-term interference from time-frequency distribution (TFD) of the received array signals. Then, the single source which shows maximum energy concentration at the corresponding auto-term TF points is extracted through three operations: i) selecting the single source auto-term TF points from the auto-term ones;ii) constructing the spatial TFD matrix according to the selected single source auto-term TF points; iii) obtaining the single source by decomposing the eigenvalue of their spatial TFD matrix. Finally, the extracted single signal is excluded by the tightening process from the received array signals, and each single signal is separated successively by repeating the above steps. In addition, a signal processing model which can describe the physical characteristics of the target echoes is established based on the separated signal components. Simulations illustrate that the image morphological filter can remove the cross-term interference and improve the TF resolution of the Wigner-Ville distribution. Anechoic pool experimental results show that the TF domain blind source extraction algorithm can well separate each target acoustic scattering component, it can also achieve a higher output signal-to-noise ratio. Furthermore, the separated elastic acoustic scattering components are in good agreement with the results computed by the surface wave generating theory, so the method can provide the robust and reliable feature for underwater target recognition.%目标声散射机理及其散射特性为识别目标的物理依据。针对水下目标声散射成分在时-频域存在相互混叠干扰，造成目标弹性声散射特征不稳定的问题，提出一种适合在欠定问题下分离目标声散射成分的时频域盲抽取方法。研究声散射成分的时频特征差异，构造目标回波单源自项的空间时频分布矩阵，通过对其进行特征值分解抽取相应的声散射成分，建立描述目标声散射物理特性的信号模型。抽取出的目标各弹性波分量与以表面环绕波产生理论计算结果相符。仿真与消声水池实验数据处理结果表明，该算法可以分离出目标回波的各个声散射成分，提高了分离信号的输出信噪比，为水下目标识别提供稳定和可靠的特征。
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