Automatic Dependent Surveillance-Broadcast Data Research Articles

A Deep Learning Approach for Predicting Aerial Suppressant Drops in Wildland Firefighting Using Automatic Dependent Surveillance–Broadcast Data

This study utilizes Automatic Dependent Surveillance–Broadcast (ADS-B) data sourced by the OpenSky Network to curate a dataset aimed at enhancing the precision of aerial suppressant drop predictions in wildland firefighting. By amalgamating ADS-B data with Automated Telemetry Unit (ATU) drop information, this research constructs a reliable base for analyzing the spatial aspects of aerial firefighting operations. Using sequential machine learning models, specifically Long Short-Term Memory (LSTM) networks and 1D Convolutional Neural Networks (1DCNN), the study interprets complex flight dynamics to predict drop locations. The dataset, covering 2017 to 2023, is labeled and segmented to reflect accurate suppressant release events, facilitating the distinction between drop and non-drop activities in fixed-wing aircraft. The LSTM model demonstrated strong predictive performance with an F1 score of 0.922, effectively identifying suppressant drop events with high accuracy. This model’s reliable predictions can significantly improve situational awareness in real-time aerial firefighting operations, enabling more informed decision-making and better coordination of resources during wildfire events.

Transfer Learning-Based Specific Emitter Identification for ADS-B over Satellite System

In future aviation surveillance, the demand for higher real-time updates for global flights can be met by deploying automatic dependent surveillance–broadcast (ADS-B) receivers on low Earth orbit satellites, capitalizing on their global coverage and terrain-independent capabilities for seamless monitoring. Specific emitter identification (SEI) leverages the distinctive features of ADS-B data. High data collection and annotation costs, along with limited dataset size, can lead to overfitting during training and low model recognition accuracy. Transfer learning, which does not require source and target domain data to share the same distribution, significantly reduces the sensitivity of traditional models to data volume and distribution. It can also address issues related to the incompleteness and inadequacy of communication emitter datasets. This paper proposes a distributed sensor system based on transfer learning to address the specific emitter identification. Firstly, signal fingerprint features are extracted using a bispectrum transform (BST) to train a convolutional neural network (CNN) preliminarily. Decision fusion is employed to tackle the challenges of the distributed system. Subsequently, a transfer learning strategy is employed, incorporating frozen model parameters, maximum mean discrepancy (MMD), and classification error measures to reduce the disparity between the target and source domains. A hyperbolic space module is introduced before the output layer to enhance the expressive capacity and data information extraction. After iterative training, the transfer learning model is obtained. Simulation results confirm that this method enhances model generalization, addresses the issue of slow convergence, and leads to improved training accuracy.

A method to mitigate cyber exploits on automatic dependent surveillance-broadcast (ADS-B) data transmissions

PurposeAutomatic dependent surveillance-broadcast (ADS-B) is the foundational technology of the next generation air transportation system defined by Federal Aviation Authority and is one of the most precise ways for tracking aircraft position. ADS-B is intended to provide greater situational awareness to the pilots by displaying the traffic information like aircraft ID, altitude, speed and other critical parameters on the Cockpit Display of Traffic Information displays in the cockpit. Unfortunately, due to the initial proposed nature of ADS-B protocol, it is neither encrypted nor has any other innate security mechanisms, which makes it an easy target for malicious attacks. The system is vulnerable to various active and passive attacks like message ingestion, message deletion, eavesdropping, jamming, etc., which has become an area of concern for the aviation industry. The purpose of this study is to propose a method based on modified advanced encryption standard (AES) algorithm to secure the ADS=B messages and increase the integrity of ADS-B data transmissions.Design/methodology/approachThough there are various cryptographic and non-cryptographic methods proposed to secure ADS-B data transmissions, it is evident that most of these systems have limitations in terms of cost, implementation or feasibility. The new proposed method implements AES encryption techniques on the ADS-B data on the sender side and correlated decryption mechanism at the receiver end. The system is designed based on the flight schedule data available from any flight planning systems and implementing the AES algorithm on the ADS-B data from each aircraft in the flight schedule.FindingsThe suitable hardware was developed using Raspberry pi, ESP32 and Ra-02. Several runs were done to verify the original message, transmitted data and received data. During transmission, encryption algorithm was being developed, which has got very high secured transmission, and during the reception, the data was secured. Field test was conducted to validate the transmission and quality. Several trials were done to validate the transmission process. The authors have successfully shown that the ADS-B data can be encrypted using AES algorithm. The authors are successful in transmitting and receiving the ADS-B data packet using the discussed hardware and software methodology. One major advantage of using the proposed solution is that the information received is encrypted, and the receiver ADS-B system can decrypt the messages on the receiving end. This clearly proves that when the data is received by an unknown receiver, the messages cannot be decrypted, as the receiver is not capable of decrypting the AES-authenticated messages transmitted by the authenticated source. Also, AES encryption is highly unlikely to be decrypted if the encryption key and the associated decryption key are not known.Research limitations/implicationsImplementation of the developed solution in actual onboard avionics systems is not within the scope of this research. Hence, assessing in the real-time distances is not covered.Social implicationsThe authors propose to extend this as a software solution to the onboard avionics systems by considering the required architectural changes. This solution can also bring in positive results for unmanned air vehicles in addition to the commercial aircrafts. Enhancement of security to the key operational and navigation data elements is going to be invaluable for future air traffic management and saving lives of people.Originality/valueThe proposed solution has been practically implemented by developing the hardware and software as part of this research. This has been clearly brought out in the paper. The implementation has been tested using the actual ADS-B data/messages received from using the ADS-B receiver. The solution works perfectly, and this brings immense value to the aircraft-to-aircraft and aircraft-to-ground communications, specifically while using ADS-B data for communicating the position information. With the proposed architecture and minor software updates to the onboard avionics, this solution can enhance safety of flights.

Development of a machine learning model for predicting abnormalities of commercial airplanes

Airplanes are a social necessity for movement of humans, goods, and other. They are generally safe modes of transportation; however, incidents and accidents occasionally occur. To prevent aviation accidents, it is necessary to develop a machine-learning model to detect and predict commercial flights using automatic dependent surveillance–broadcast data. This study combined data-quality detection, anomaly detection, and abnormality-classification-model development. The research methodology involved the following stages: problem statement, data selection and labeling, prediction-model development, deployment, and testing. The data labeling process was based on the rules framed by the international civil aviation organization for commercial, jet-engine flights and validated by expert commercial pilots. The results showed that the best prediction model, the quadratic-discriminant-analysis, was 93% accurate, indicating a “good fit”. Moreover, the model’s area-under-the-curve results for abnormal and normal detection were 0.97 and 0.96, respectively, thus confirming its “good fit”.

Advanced collision risk estimation in terminal manoeuvring areas using a disentangled variational autoencoder for uncertainty quantification

Air Traffic Management aims at ensuring safety during aircraft operations, particularly within Terminal Manoeuvring Areas where traffic density is high. The challenge lies in balancing safety and efficiency by closely managing the likelihood of mid-air collisions regarding the airport movements. Traditional models like the Reich and Anderson-Hsu models have been influential, but they fall short in representing the complex reality of Terminal Manoeuvring Areas. Data-driven approaches are emerging, with Monte Carlo simulations offering a more flexible methodology for collision risk estimation. This paper introduces a framework for assessing Mid-Air Collision likelihood resulting from Terminal Manoeuvring Area procedures by combining the field of Deep Generative Modelling using a Variational Autoencoder with the domain of rare event statistics through Subset Simulation. By incorporating disentanglement into the Variational Autoencoders model, we create a latent space that aligns dimensions with distinctive trajectory traits. Then, Subset Simulation is employed to gauge Mid-Air Collision probability, utilizing latent representations as input. Finally, sensitivity analysis reveals pivotal factors influencing collision risk, correlated with trajectory attributes via disentanglement. The methodology is applied to traffic around Zurich Airport: it evaluates the risk arising from go-around and take-off procedures using Automatic Dependent Surveillance-Broadcast data.

Automatic Dependent Surveillance-Broadcast Deceptive Jamming Detection Method Based on Track Data

The characteristics of automatic dependent surveillance-broadcast (ADS-B) technology, such as unencrypted channels and open data protocols, make it extremely vulnerable to various intentional or unintentional spoofing and jamming, which seriously threatens air traffic safety. Traditional approaches do not update their high-bit data for a long time, which causes the situations of the same encoding results to have different aircraft positions and it is difficult to determine the real position of the aircraft and effectively detect ADS-B jamming. Therefore, it is urgent to propose more effective detection methods for ADS-B spoofing and jamming. First, the methods and principles of frequency shift calculation and automatic monitoring algorithm are introduced. Then, signal consistency detection and error robustness are analyzed, and a track-data-based detection model for abnormal ADS-B data are proposed. The track-data-based detection methods can use multi-point sampling judgment algorithm to realize the reception and processing of data bits. This method makes full use of the information of multiple sampling values of each data bit and the reference power value obtained in the preamble detection, and determines the data value and confidence value through the relationship between the two. The detection methods for ADS-B spoofing and jamming distinguishes the true and false signals and the authenticity of target positions by comparing the arrival time difference of each signal at the ground station. Finally, the research results show that, with the increase of signal to noise ratio, no matter the estimation based on virtual array translation or the estimation based on circular stationary signal, their estimation accuracies are improved with gradually increasing trends.

RESEARCH ON CHAOTIC CHARACTERISTICS AND SHORT-TERM PREDICTION OF EN-ROUTE TRAFFIC FLOW USING ADS-B DATA

The short-term traffic flow prediction can help to reduce flight delays and optimize resource allocation. Using chaos dynamics theory to analyze the chaotic characteristics of en-route traffic flow is the basis of short-term en-route traffic flow prediction and ensuring the orderly and smooth state of the en-route. This paper takes the time series of en-route traffic flow extracted from Automatic-Dependent Surveillance Broadcast (ADS-B) measured data as the research object, uses the improved C–C method to reconstruct the phase space, and uses the improved small data volume method to calculate the Lyapunov index to identify the chaos phenomenon of en-route traffic flow. In order to avoid the interference of chaos phenomenon on traffic prediction, the Wavelet Neural Network (WNN) model is established to predict the traffic flow at en-route points. The experimental shows that when the number of iterations is 10,000, the average accuracy of WNN prediction is 0.87173, and the average running time is 6.9335334[Formula: see text]s. According to the experimental results, it can be seen that the smaller number of iterations has more advantages in running time, which greatly reduces the overall running time. At the same time, it indicates that appropriately increasing or reducing the number of iterations in this experiment has little effect on the results.

Aircraft trajectory prediction and aviation safety in ADS-B failure conditions based on neural network

With the rapid expansion of transportation demand, the number of global flights has rapidly increased, which also poses challenges to air traffic management (ATM). Considering that the radar system in ATM can no longer meet the requirements of flight safety, a very promising next-generation air traffic control technology—Automatic Dependent Surveillance Broadcast (ADS-B) technology has been introduced. However, in the event of on-board equipment failure and local area signal interference, the ADS-B’s signal will disappear or be interrupted. This sudden situation can pose a danger to aviation safety. To solve this problem, this article proposes a bidirectional long short-term memory (Bi-LSTM) network prediction method combining historical ADS-B data to short-term predict the trajectory of aircraft, which can improve aviation safety in busy airspace. Firstly, the problem of frequent dynamic modeling of different types of aircraft was solved by utilizing historical ADS-B data as the data source. Secondly, the data cleansing method is proposed for ADS-B raw data. Furthermore, considering that the spatial trajectory of the aircraft is a complex time series with continuity and interactivity, a bidirectional LSTM based aircraft trajectory prediction framework is proposed to further improve prediction accuracy. Finally, a trajectory with frequent changes was selected for prediction, and compared with 7 prediction methods. The results showed that the proposed method had high prediction accuracy, thus also improving the aviation safety of the aircraft.

Construction of a model of steganographic embedding of the UAV identifier into ADS-B data

Secure data exchange in the control system of unmanned aerial vehicles (UAVs) is an important aspect for preventing unauthorized access and safety of aerial vehicles. Given the problems of automatic dependent surveillance-broadcast (ADS-B) data protection, the safety level of UAV flight tasks and air traffic in general is significantly reduced. Therefore, the protection of ADS-B data is an urgent task. The object of the study is the process of steganographic protection of ADS-B format data. A relevant problem of estimating the probabilistic time characteristics of the steganographic protection process is solved, taking into account the features of data embedding in the ADS-B format container. To solve it, a mathematical formalization of the methods of finding probabilistic-temporal characteristics of steganographic systems was carried out. A model of steganographic data transformation operations based on the Chinese remainder theorem has been built. The main difference of the model is taking into account the features of the ADS-B format data. This made it possible to formalize and evaluate the time functions of steganographic encoding and decoding of UAV identifiers with an integrated ADS-B system. A model of steganographic data transformation operations based on the finite integral ring theorem has been constructed. A list of operations performed in the developed algorithm has been compiled. This made it possible to carry out mathematical formalization of operations for complex use in the model of steganographic protection of UAV identifiers with a built-in ADS-B system. The mathematical model was studied and the estimation of the random value of the time of steganographic transformation of data, as well as the confidence interval, was performed. With the help of the reported set of models, it is possible to estimate the probability of the algorithm’s execution time falling within the given interval. The results of the calculation of probabilistic-time characteristics could be used in models of a higher level of the hierarchy

On the Causes and Environmental Impact of Airborne Holdings at Major European Airports

This paper introduces a data-driven technique for labelling airborne holdings based on their underlying causes, specifically distinguishing between adverse weather conditions and other causes, such as airport capacity. Utilising a dataset comprised of flight trajectories arriving at 45 European airports over a nine-month period, extracted from automatic dependent surveillance-broadcast data, this paper provides valuable insights into the causes behind airborne holdings and their relative environmental impact. The proposed approach involves employing an existing neural network to identify airborne holdings. Subsequently, these holdings are cross-referenced with actual weather observations obtained from meteorological aerodrome reports. Following this, a subset of the holdings is labelled as either weather-related or attributed to other causes, based on historical air traffic flow management regulations. Finally, the cause of the majority of unlabelled holdings is determined using semi-supervised learning. The findings indicate that at least one-quarter of the 30-minute time periods with airborne holdings identified by the neural network can be attributed to weather-related factors, with reduced visibility, strong winds, and convective weather, emerging as the primary contributing events. Intriguingly, weather-related causes account for approximately 40% of the total fuel consumption associated with these procedures.

A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)

With the exponential increase in the volume of automatic dependent surveillance-broadcast (ADS-B), and other types of air traffic control (ATC) data containing spatiotemporal attributes, it remains uncertain how to respond to immediate ATC data access within a target area. Accordingly, an original multi-level disaggregated framework for airspace, and its corresponding information management is proposed. Further, a multi-scale grid modeling and coding mapping method of airspace information represented by ADS-B is put forth. Finally, tests on the validity of the 4D airspace-temporal grid we named as the GeoSOT-AS framework were conducted across key areas based on the development of an effective data organization method for ADS-B, or an effective algorithm for extracting relevant spatiotemporal data. Experimentally, it was demonstrated that GeoSOT-AS conforms to the existing Chinese specification of civil aeronautical charting and is advantageous for its low deformation and high practicality; furthermore, the airspace grid identification code modeling was less costly, and improved performance by >80% when used for ADS-B data extraction. GeoSOT-AS can thus provide effective reference and practical information for existing airspace data management methods represented by ADS-B and can subsequently be extended to other forms of airspace management scenarios.

ADS-B data usage for aircraft noise and air quality modelling and measurement during specific stages of LTO cycle

The paper is targeted at the analysis of the importance of Automatic Dependent Surveillance-Broadcast (ADS-B) data in the terms of overcoming gaps between aircraft noise and local air quality modelling results and their short or long-term measurements. Presented results based on noise and air pollution measurement campaign at Ukrainian airports describe general peculiarities of the pre-processing and usage of ADS-B data during specific stages of aircraft landing and taking-off (LTO) cycle in the airport. The outcomes could be used for more accurate noise and air pollution exposure assessment and the development of recommendations for noise and local air quality monitoring systems in airports under consideration.

COMPARATIVE ANALYSIS OF DATA TRANSMISSION MODES 1090ES AND VDL MODE 4 FOR ADS-B ANTENNAS

The study and analysis of technologies affecting the safety of aircraft flights is relevant and important in a strategic plan. One such technology is Automatic Dependent Surveillance-Broadcast (ADS-B), a technology that allows aircraft pilots and air traffic controllers to receive the most accurate information about aircraft movements. It is currently being implemented in many countries around the world. The VDL-4 message format provides the ability to send message types not described in the standard. This is achieved through the use of spare bits in the fields of service messages. Data communication is described as a presentation layer service that allows information such as the year to be transmitted, or work with the protocols of a self-organizing network to increase its coverage area. The article examines the data transmission lines currently in use for ADS-B. As a result of the work, a comparative analysis of current ADS-B data lines: 1090ES and VDL Mode 4 is presented.

Aircraft Flight Movement Anomaly Detection using Automatic Dependent Surveillance-Broadcast

Automatic Dependent Surveillance-Broadcast (ADS-B) is an aircraft backup radar device that transmits aircraft sensor information via radio frequency. This data can be used to detect aircraft changes that occur significantly or abnormally (anomaly). Anomaly detection in this study aims to reduce and prevent flight accidents by analyzing abnormal data on aircraft flights using the Deep Learning (DL) model. Bidirectional LSTM (Bi-LSTM) and Bidirectional GRU (Bi-GRU) models are proposed as DL models which are implemented on ADS-B data using data mining methods. The data is generated from the ADS-B device that records the plane crash incident and is stored on the Flightradar24 community server. Data containing sensor changes from anomalous aircraft movements are studied for predictability on other flight data. The class breakdown is divided into two, anomaly and normal, based on information on the time span of anomaly occurrences in the accident investigation report of each aircraft using the sliding window technique in the data mining methodology. In evaluation, the confusion matrix measurement method is used to predict predictive analysis of the tested data. The results of the model evaluation performance show that the Bi-LSTM proposed in this study has the best accuracy of 99.44% and the f1-score of 99.51% is slightly superior to the Bi-GRU model. The model in this study can be applied in the ADS-B device to detect aircraft movement anomalies and as material for reviewing technicians in periodic maintenance and measuring the accuracy of the ADS-B device used as a backup radar.

A METHOD FOR AUTOMATIC AIRPORT OPERATION COUNTS USING CROWD-SOURCED ADS-B DATA

Airports are tasked with counting and reporting their operations at least yearly. The counts are used at the local and national level to schedule maintenance, for research, and to receive funds, making their accuracy important. Historically, methods for counting operations at non-towered airports have relied on additional equipment at the airport or statistical estimates. In this work, we introduce a method to use crowd-sourced Automatic Dependent Surveillance – Broadcast (ADS-B) data from the OpenSky network to automatically count airport operations and report it separated by takeoffs and landings. We use two airports as case studies – Tulsa International Airport (TUL) and Purdue University Airport (LAF) – and compare the estimated operation counts from the ADS-B data algorithm to numbers reported through the Federal Aviation Administration’s (FAA) Air Traffic Activity Data System (ATADS).

Usage Monitoring of Helicopter Gearboxes with ADS-B Flight Data

Health and usage monitoring systems (HUMS) are the basis for condition-based maintenance of helicopters. One of the most critical systems in terms of safety and maintenance expense that can be monitored by HUMS are the main gearboxes of helicopters with turbine engines. While the health monitoring part of HUMS aims to model the health state from the collected sensor data with advanced algorithms, such as machine learning, the usage monitoring part tracks the time of use and operating parameters of the system, such as load, to determine lifetime consumption. In the presented work, a combination of automatic dependent surveillance-broadcast (ADS-B) flight data with a generic helicopter performance model is used to acquire torque profiles of the gearboxes. With damage accumulation methods, the load spectra are transformed to aggregated indicators that reflect the individual gearbox usage. The methodology is applied to samples of two helicopters from a five-year ADS-B data set of German helicopter emergency medical services (HEMS) acquired for the study. The results demonstrate the feasibility of the generic approach, which can support maintenance scheduling and new usage-based maintenance services independent of direct access to installed HUMS.

Securing ADS-B data transmissions using blockchain: a comprehensive survey and analysis

PurposeThe purpose of this paper is to study the various solutions and recommendations provided by researchers in applying the blockchain concept to different problems in aviation industry. It will discuss and highlight the specific approaches that leverages blockchain to mitigate the automatic dependent surveillance-broadcast (ADS-B) security issues. Furthermore, it introduces an innovative design and method to secure ADS-B data using a tamper-resistant distributed public ledger of authenticated flight plans and validates the position and other parameters of the associated aircraft identifier against the flight paths/routes that are stored in the blockchain ledger.Design/methodology/approachADS-B is the key technology that is mandated by Federal Aviation Administration in USA by 2020. However, ADS-B data is neither encrypted nor authenticated. This paper proposes a novel solution using blockchain to secure the ADS-B data communications and in-depth analysis of existing solutions covering the following aspects: classification of various possible attacks on ADS-B. Presents various solutions proposed by different researchers regarding use of blockchain in aviation industry. Discuss a new solution to secure ADS-B using blockchain. Discuss the high-level architectural framework of the proposed and patented solution. Finally, presents the conclusions and future work scope.FindingsWhile the main intention of this paper is to bring together all the existing solutions using blockchain to secure aviation and ADS-B data at one place, the proposed novel solution could contribute to maintaining security and privacy for aircrafts flying in the airspace at any point in time. Continuously securing the ADS-B data transmissions based upon the filed flight plans in real time can provide a mechanism to identify spoofed aircraft messages and communicate the same to ground stations for authentication of existence of such a malicious aircraft. Thus, this solution also differs from all the existing ones.Practical implicationsAs aviation industry is in its infancy stage in implementing blockchain-based solutions, practical implementation of the proposed concept might take longer.Originality/valueThis paper is a comprehensive survey and review paper. To the best of the authors’ knowledge, this is the first of its kind that presents various use cases for usage of blockchain in aviation industry along with a detailed review of existing proposed or implemented solutions using blockchain to secure ADS-B data. This can serve as an invaluable reference for the future researchers on this topic in both industry and academia.

ADS-B-Based Spatiotemporal Alignment Network for Airport Video Object Segmentation

Video object segmentation (VOS) is the fundamental problem of vision-based intelligent transportation, and many VOS algorithms relying on inference from reference masks have been proposed. Due to the inherent defects of the inference strategy and the complex changes of targets, VOS methods that perform well on public datasets are usually ineffective in airport scenarios. We propose a spatiotemporal alignment network (STA-Net) that makes use of Automatic Dependent Surveillance-Broadcast (ADS-B) data as prior information to guide the long-term segmentation of aircraft. ADS-B is an airport-specific signal, which indicates the location of aircraft in real time. Based on ADS-B, we continuously generate new reference masks instead of using previous masks for inference, which greatly reduces the accumulation of inference errors. To achieve this, previous masks of each aircraft are aligned on the temporal domain based on the position information in ADS-B. All temporally-aligned masks are compared, and the one most similar to the current instant is reserved. This mask is both temporally and spatially aligned; hence it is a better reference mask for inference. Aligned masks are updated every time new ADS-B data arrive, so that they can support long-term inference. With the selected mask as a reference, aircraft of interest are segmented within a unified encoder-decoder framework over the long term. Experiments on a benchmark dataset and in a real airport scenario verify the effectiveness of the presented method.

Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna

Automatic Dependent Surveillance-Broadcast (ADS-B) signals are very vital in air traffic control. However, the space-based ADS-B signals are easily overlapped and their message cannot be correctly received. It is challenge to separate overlapped signals especially for a single antenna. The existing methods have a low decoding accuracy for small power difference, carrier frequency difference and relative time delay between overlapped signals. In order to solve these problems, we apply the deep learning method to single antenna ADS-B signal separation. A multi-scale Conv-TasNet (MConv-TasNet) is proposed to capture long temporal information of the ADS-B signal. In MConv-TasNet, a multi-scale convolutional separation (MCS) network is proposed to fuse different scale temporal features extracted from overlapping ADS-B signals and generate an effective separation mask to separate signals. Moreover, a large dataset is created by using the real ADS-B data. In addition, the proposed method has been evaluated on the dataset. The average decoding accuracy on the test set is 90.34%. It has achieved the state-of-the-art results.

Development of a Reliable Method for General Aviation Flight Phase Identification

Aircraft operations statistics have typically received significant attention from U.S. airport owners and operators and state, local, and federal agencies. Accurate operational data is beneficial in assessing airports’ performance efficiency and impact on the environment, but operational statistics at nontowered general aviation airports are, for the most part, limited or not available. However, the increasing availability and economy of capturing and processing Automatic Dependent Surveillance-Broadcast (ADS-B) data shows promise for improving accessibility to a wide variety of information about the aircraft operating in the vicinity of these airports. Using machine learning technology, specific operational details can be decoded from ADS-B data. This paper aims to develop a reliable and economical method for general aviation aircraft flight phase identification, thereby leading to improved noise and emissions models, which are foundational to addressing many public concerns related to airports.