NextGen ADS-B Software-Defined Reception with Enhanced Techniques

Markani, Jamal Habibi; Amrhar, Abdessamad; Gagné, Jean-Marc; Landry, René

doi:10.3390/app13010493

Cited by 4 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in most cases, the output accuracy should be better. The majority of current research uses automatic dependent surveillance-broadcast (ADS-B) [7] [8] as the main source of their data, which can contain many gaps within each flight and noise. In contrast, we leverage complete flight data extracted from actual flights enabling our model to better understand the variations during flight and deliver stable and consistent results.…”

Section: Introductionmentioning

confidence: 99%

A Meta-Learning Approach for Aircraft Trajectory Prediction

Rizvi¹,

Markani²,

Landry³

2023

View full text Add to dashboard Cite

The aviation industry has seen significant advancements in safety procedures over the past few decades, resulting in a steady decline in aviation deaths worldwide. However, the safety standards in General Aviation (GA) are still lower compared to those in commercial aviation. With the anticipated growth in air travel, there is an imminent need to improve operational safety in GA. One way to improve aircraft and operational safety is through trajectory prediction. Trajectory prediction plays a key role in optimizing air traffic control and improving overall flight safety. This paper proposes a meta-learning approach to predict short-to mid-term trajectories of aircraft using historical real flight data collected from multiple GA aircraft. The proposed solution brings together multiple models to improve prediction accuracy. In this paper, we are combining two models, Random Forest Regression (RFR) and Long Short-term Memory (LSTM), using k-Nearest Neighbors (k-NN), to output the final prediction based on the combined output of the individual models. This approach gives our model an edge over single-model predictions. We present the results of our meta-learner and evaluate its performance against individual models using the Mean Absolute Error (MAE), Absolute Altitude Error (AAE), and Root Mean Squared Error (RMSE) evaluation metrics. The proposed methodology for aircraft trajectory forecasting is discussed in detail, accompanied by a literature review and an overview of the data preprocessing techniques used. The results demonstrate that the proposed meta-learner outperforms individual models in terms of accuracy, providing a more robust and proactive approach to improve operational safety in GA.

show abstract

Section: Introductionmentioning

confidence: 99%

A Meta-Learning Approach for Aircraft Trajectory Prediction

Rizvi¹,

Markani²,

Landry³

2023

View full text Add to dashboard Cite

show abstract

“…In contrast to legacy solutions, LSTM-based model accuracy for packets with over 12 bits of error is over 60%, while these sorts of packages are simply discarded in legacy algorithms. Additionally, the LSTM-based error correction model is quite compatible with SDR-based ADS-B receivers and it can enhance the performance of ADS-B In, enabling it to detect more sensitive data[9].The proposed model is designed based on ADS-B's various messages' features and their transmission periods. In addition, data cleaning and the removing of outliers are done during the data preparation period.…”

mentioning

confidence: 99%

ADS-B Reception Error Correction Based on the LSTM Neural-Network Model

Markani¹,

Rizvi²,

Amrhar³

et al. 2023

Self Cite

View full text Add to dashboard Cite

Standard automatic dependent surveillance broadcast (ADS-B) reception algorithms offer considerable performance at high signal-to-noise ratios (SNRs). However, the performance of ADS-B algorithms in applications can be problematic at low SNRs and in high interference situations, as detecting and decoding techniques may not perform correctly in such circumstances. In addition, conventional error correction algorithms have limitations in their ability to correct errors in ADS-B messages, as the bit and confidence values may be declared inaccurately in the event of low SNRs and high interference. The principal goal of this paper is to deploy a Long Short-Term Memory (LSTM) recurrent neural network model for error correction in conjunction with a conventional algorithm. The data of various flights are collected and cleaned in an initial stage. The clean data is divided randomly into training and test sets. Next, the LSTM model is trained based on the training dataset, and then the model is evaluated based on the test dataset. The proposed model not only improves the ADS-B In packet error correction rate (PECR), but it also enhances the ADS-B In terms of sensitivity. The performance evaluation results reveal that the proposed scheme is achievable and efficient for the avionics industry. It is worth noting that the proposed algorithm is not dependent on conventional algorithms' prerequisites.

show abstract