## 1 Introduction  
When underwater acoustic homing weapons (UAHWs) employ active sonar for detecting, locating, tracking, and targeting submarines, they encounter the challenge of dealing with a variety of acoustic decoys deployed by the submarines to mislead them. To ensure the successful targeting of submarines, UAHWs must be equipped with active target recognition capabilities. This enables them to effectively identify submarines and counter various acoustic decoys. Scholars have undertaken research on real and decoy target recognition technology for UAHWs. According to [1], the essential principle behind real and decoy target recognition technology for active acoustic homing is the differentiation of real targets from \"decoy targets\" generated by underwater acoustic countermeasures based on their scale characteristics. Hence, scale characteristics play a pivotal role in the initial target recognition process of underwater acoustic homing weapons. Early researchers successfully accomplished scale target recognition by employing the azimuth analysis method [2, 3]. Nevertheless, as underwater acoustic countermeasures have advanced, acoustic decoys have transitioned from point source decoys to scale decoys equipped with extensive towed arrays. This evolution has rendered it unfeasible to distinguish scale decoys from submarines solely based on scale features [4, 5]. Consequently, there is an urgent imperative to develop a target recognition method for scale decoys in UAHWs. In recent years, the rapid advancement of artificial intelligence (AI) has provided new avenues for target recognition. Conducting research on intelligent acoustic target recognition holds significant practical importance. Currently, AI and machine learning technologies have been widely applied to acoustic target recognition issues [6; 7]. Underwater acoustic target recognition methods based on AI can be divided into statistical machine learning methods and deep learning methods. Among them, the object recognition method based on statistical machine learning includes support vector machine [8; 9] and other methods [10], while the object recognition method based on deep learning includes convolutional neural networks [11; 12; 13; 14; 15], long short-term memory [16], and other methods [17]. It can be observed that for passive underwater acoustic target recognition, scholars have conducted a substantial amount of research. However, there has been relatively less research on active target recognition methods based on AI. Due to its ability to efficiently address various real-world problems, ensemble learning has garnered significant attention in the field of machine learning, and the stacking ensemble technique is an important method within ensemble learning. Stacking ensemble learning can integrate the strengths of various traditional machine learning methods, exhibiting strong generalization abilities with minimal impact on time and space complexity. Therefore, stacking ensemble learning is well suited for applications in scenarios with high real-time requirements. Currently, the stacking ensemble technique has been applied in various fields, including rainfall prediction [18], ocean wave height prediction [19], rock mass classification [20], and damage prediction [21]. In this study, we presented a real-time target recognition method for UAHWs based on the stacking ensemble technique. It was designed to address the issue of identifying submarines and scale decoys in UAHWs. Firstly, under the active wideband detection waveform illumination of UAHWs, submarines exhibited the characteristic of multi-highlight surface reflection, while scale decoys exhibited the characteristic of multi-highlight point reflection. From the output of target broadband signal correlation detection, we extracted statistical features of target echo energy distribution and spatial distribution. Next, we constructed a stacking ensemble model suitable for UAHWs.