# Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method
## 1 Introduction  
With the continuous development of human scientific and technological civilization, the resources and spaces in the ocean have become more important as a proportion of national resource development. The development of marine resources and the protection of maritime rights and interests are integral parts of achieving a solid ocean state, which is of great strategic importance for all countries [1, 2]. At the same time, to build a power ocean, it is imperative to develop appropriate underwater detection equipment and related technologies; underwater positioning technology is the most critical underwater detection technology, and how to perform accurate positioning in the complex ocean environment is a problem that needs to be urgently solved. In traditional underwater detection technology, acoustic-based underwater localization techniques have been widely studied by researchers [3, 4, 5]. They have achieved good localization results, but near-field targets are susceptible to mutual interference, such as noise between marks [6, 7]. Sonar and visual detection technology in a specific environment (narrow space, turbid waters) is ineffective. Due to the natural shielding effects of seawater, the GPS positioning of land navigation cannot be used for underwater positioning. The complex environment, such as ocean noise, interferes significantly with the perceived effect of sonar, and the traditional optical detection equipment is limited by underwater refraction, insufficient light, and other factors [8, 9]. To solve these problems, the underwater detection equipment can sense the target position in the near-field environment and guarantee the successful execution of the mission of underwater detectors. The development of a new underwater positioning technology is an urgent need. Fish and some amphibians have susceptible underwater perceptions of the lateral line system on both sides of the fish body. The lateral line system is an essential organ for fish and these amphibians to obtain information about their surroundings [10; 11]. With the help of this organ, fish and these amphibians can perform behaviors, such as clustering, prey localization, obstacle avoidance, and target recognition in complex underwater environments. The aquatic environment is complex and variable. When light conditions in the external environment are insufficient, fish rely on their unique lateral line perception systems to sense information about the surrounding flow field [12; 13; 14]. A more representative one is the Mexican cavefish, which has lived in this way under dark conditions for a long time [15]. The lateral line system of fish consists of two main components: the superficial neuromast, which perceives the flow velocity in the near-field environment, and the canal neuromast, which is sensitive to pressure changes in the near-field environment [16], as shown in Figure 1. The perceptual functions of these two structures differ because of their different distributions on the lateral line system and differences in their structuring [17; 18]. It has been shown that the superficial neuromast is more sensitive to velocity changes in the flow field. In contrast, the canal neuromast can respond better to pressure changes in the flow field environment [19]. Based on the properties and mechanisms of the fish lateral line system to perceive the surrounding flow field, it provides a new idea for the underwater localization of the underwater detector to accurately perceive the surrounding near-field environment. In this paper, we propose using an array of integrated pressure sensors to locate dipole sources in the near-field. Similar to the lateral fish line, the sensor arrays are arranged in parallel and are more compact compared to the cross-type arrangement. In addition, based on the collected pressure variation data, we constructed a multilayer perceptron (MLP) neural network algorithm for dipole source localization.