# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 1 Introduction  
Historical statistical data indicate that compared to the past few decades, the proportion of mid-air collisions in aviation accidents is much smaller. This reduction can be attributed not only to significant improvements in display technologies, radar performance, and data processing, but also to the implementation of new safety defense systems, such as the traffic collision avoidance system (TCAS) and the short-term conflict alert (STCA). However, as mid-air collision is among the most serious incidents in air traffic, the study of collision risk becomes a primary focus in air traffic management systems. The main task of air traffic management is to control risk and ensure safety. Essentially, safety is regarded as reducing risk and maintaining it at an acceptable level. The International Civil Aviation Organization (ICAO) introduced the concept of a target level of safety (TLS) for flight safety. Focusing on collision prevention goals within air traffic management system and based on analyses of aircraft proximity events, [PERSON] [1] summarized the safety protection layers or risk sources of air traffic management systems into three aspects: (1) air traffic control regimes, management rules and procedures, and airspace structure, (2) an airborne collision avoidance system and a ground conflict alert system, and (3) controllers' and pilots' monitoring of traffic situations and timely intervention in case of danger. Vulnerabilities in each link of the safety protection layers can become causes of accident. Focusing on urban logistics UAVs in free flight, to ensure the safety of operation, it is urgent to strengthen UAV conflict management and to have autonomous separation maintenance capability. To achieve autonomous separation maintenance, it is necessary to undertake track prediction and formulate the required separation. The core task is to determine the necessary separation based on track prediction error, air traffic service capability, conflict detection and resolution mechanisms, and other maneuverability performance parameters. Research has been conducted on two aspects. In the field of track prediction, current studies have applied machine learning models to address short-term track prediction issues. [PERSON] et al. [2] successfully used a machine learning model to improve climb prediction based on ground applications, demonstrating the feasibility of altitude prediction. [PERSON] et al. [3] proposed a sequence-to-sequence deep long short-term memory network for track prediction. [PERSON] et al. [4] introduced a new hybrid architecture for 4D track prediction based on deep learning, combining convolutional neural networks (CNNs) and long short-term memory (LSTM). [PERSON] et al. [5] proposed a hybrid model combining deep feed-forward neural networks with LSTM for track prediction, where the uncertainty of model predictions is addressed using Bayesian methods to enhance in-flight safety. [PERSON] et al. [6] developed a 4D track prediction method based on aircraft performance data and aircraft intentions. [PERSON] et al. [7] improved the Kalman filtering algorithm to enhance prediction accuracy by adjusting current position data in real time. [PERSON] et al. [8] constructed a multi-information extended Kalman filtering prediction system to achieve 4D track predictions that are more consistent with actual aircraft movements and provide higher precision. [PERSON] et al. [9] proposed a short-term 4D track prediction algorithm based on online updating of LSTM networks to enable real-time model parameter updates, making the model robust. [PERSON] et al. [10] used a hybrid deep learning approach for track prediction based on ADS-B data. [PERSON] [11] conducted a review of existing track prediction issues. [PERSON] et al. [12] used LSTM to predict aircraft climb and descent phases in track prediction. In the field of required separation research, focusing on UAV separation maintenance and collision avoidance, achievements have concentrated on gas models and Monte Carlo simulation methods.