# A Fuzzy Path Selection Strategy for Aircraft Landing on a Carrier
## 1 Introduction
A model of LSO was found for the digital simulation of a pilot-carrier system on the basis of analyzing the behaviors of LSO in the final stage of landing. Fuzzy logic laws were used to model three kinds of behaviors of the LSO. Afterwards, the carrier motion prediction of the LSO was described by a neural network model, and the model was verified by simulating landing under several kinds of conditions [16]. In the aspect of studying pilot behavior, a variable-strategy pilot model was established for landing. This model is comprised of two switchable components: a discrete component of the acquisition strategy for a large deviation of the glide path and a continuous component of the pursuit tracking strategy for a small deviation [17]. The cited literature can explain the work of LSO, and mathematical models are established to describe the LSO's action. The above studies regarding the landing process of the carrier aircraft presented solid research work on the automatic landing guidance technology and have potential significance in the landing safety of aircraft. The issue of the landing path for aircraft carrier is also an important part of the automatic landing guidance technology. Unlike the unmanned aerial vehicle (UAV) path planning problem [18], the landing path of manned aircraft is fixed and can only be selected from several candidates. Therefore, instead of planning the landing path, it is determined before the aircraft is ready to reach the carrier, and the landing path determination is also a part of the LCO's work [19; 20]. However, no relevant literature has been reported on this issue so far. Besides, the previous methods, such as the proposed method in Ref. [14; 17], are related to the behavior of the LSO and the pilot, and the goal is to reduce the error between the ideal and actual landing path as much as possible. The premise of those methods is that the landing path has been determined, and a new method has to be developed to solve the landing path selection problem. As the landing path selection is the first step to guide the aircraft landing, it is important to the whole process of the landing task and thus is worthwhile to be studied intensively. At present, the landing path of carrier aircraft is determined by the LCO's experience, and then the decision is sent to the pilot. The pilot is passive in the process of determining the landing path, which is not so reasonable because the pilot also has some important information and should have the right to make a suggestion on the selection of the landing path. Besides, the experience of the LCO is not always reliable, especially in complicated situations, and some unreasonable decisions may be made if only the knowledge and experience of a human are depended on. Therefore, a landing path selection strategy is imperative to deal with the above defects, thus reducing the workload and ensuring the landing safety. The decision should be made according to the performance of the aircraft, the traffic conditions, a variety of weather conditions and the visibility of the pilot, and fuzziness is involved in the landing path selection problem [21]. The main contribution is to establish a mathematical model and provide a method to solve the landing path selection problem. This problem is a sort of decision-making problem which belongs to the air traffic management of the carrier. To make a subjective judgment optimal and practical, a decision needs to be made based on reliable information considering all constraints. Obtaining the environmental information quickly is essential to the decision-making process in this problem. Moreover, how to process information according to knowledge and experience is the key. The problem involves various elements and fuzzy information. Therefore, it is an unstructured decision-making problem. This kind of problem can only be solved based on the experience and flexibility of a decision maker or using an AI method. It is also worth noting that different decision makers may result in different judgments due to many reasons.