ADS-BI: Compressed Indexing of ADS-B Data

Wandelt, Sebastian; Sun, Xiaoqian; Fricke, Hartmut

doi:10.1109/tits.2017.2784371

Cited by 9 publications

(7 citation statements)

References 23 publications

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“…The automatic broadcast-dependent surveillance system (ADS-B) [26] used in this paper is a new satellite-based air traffic control paradigm for connecting aircraft with ground stations. The system utilizes satellites to automatically and continuously provide 4D position data (latitude, longitude, and time), while altitude, speed, and heading information are provided by onboard aircraft equipment.…”

Section: Data Analysis and Processingmentioning

confidence: 99%

Data-Driven 4D Trajectory Prediction Model Using Attention-TCN-GRU

Ma,

Meng,

2024

Aerospace

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With reference to the trajectory-based operation (TBO) requirements proposed by the International Civil Aviation Organization (ICAO), this paper concentrates on the study of four-dimensional trajectory (4D Trajectory) prediction technology in busy terminal airspace, proposing a data-driven 4D trajectory prediction model. Initially, we propose a Spatial Gap Fill (Spat Fill) method to reconstruct each aircraft’s trajectory, resulting in a consistent time interval, noise-free, high-quality trajectory dataset. Subsequently, we design a hybrid neural network based on the seq2seq model, named Attention-TCN-GRU. This consists of an encoding section for extracting features from the data of historical trajectories, an attention module for obtaining the multilevel periodicity in the flight history trajectories, and a decoding section for recursively generating the predicted trajectory sequences, using the output of the coding part as the initial input. The proposed model can effectively capture long-term and short-term dependencies and repetitiveness between trajectories, enhancing the accuracy of 4D trajectory predictions. We utilize a real ADS-B trajectory dataset from the airspace of a busy terminal for validation. The experimental results indicate that the data-driven 4D trajectory prediction model introduced in this study achieves higher predictive accuracy, outperforming some of the current data-driven trajectory prediction methods.

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Section: Data Analysis and Processingmentioning

confidence: 99%

Data-Driven 4D Trajectory Prediction Model Using Attention-TCN-GRU

Ma,

Meng,

2024

Aerospace

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“…We also evaluate the use of interpolation to fill in large periods of missing data during the trajectory. For comparison purposes, we propose a baseline including the MVR-tree [17], but we do not include ADS-BI [21] because it does not provide altitude-based queries, and its inner index stores some string dimensions which are not covered by 3DGraCT.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Although their numbers are moderately successful, the resulting representations can not be efficiently queried. More recently, a compressed index for ADS-B (called ADS-BI) has been proposed [21]. It performs block partitioning and stores descriptive metadata about the block to enable some types of queries.…”

Section: Introductionmentioning

confidence: 99%

3DGraCT: A Grammar-Based Compressed Representation of 3D Trajectories

Brisaboa¹,

Gómez-Brandón²,

Martínez‐Prieto³

et al. 2018

String Processing and Information Retrieval

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Much research has been published about trajectory management on the ground or at the sea, but compression or indexing of flight trajectories have usually been less explored. However, air traffic management is a challenge because airspace is becoming more and more congested, and large flight data collections must be preserved and exploited for varied purposes. This paper proposes 3DGraCT, a new method for representing these flight trajectories. It extends the GraCT compact data structure to cope with a third dimension (altitude), while retaining its space/time complexities. 3DGraCT improves space requirements of traditional spatio-temporal data structures by two orders of magnitude, being competitive for the considered types of queries, even leading the comparison for a particular one.

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“…Typical tracks include the flight path of aerial targets, the navigation path of naval vessels, etc. At present, TTTA technology is widely used in engineering practice, such as Marine Vessel Automatic Identification System (AIS) [12], Automatic Dependent Surveillance-broadcast System (ADS-B) [13], and battlefield air information fusion. Especially in modern battlefields, which have expanded to the five-dimensional space of land, sea, air, space, and electromagnetic, TTTA judgment directly affects the final decision of commanders.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Anti-Bias Track-to-Track Association Algorithm Based on Nearest Neighbor Interval Average Distance for Multi-Sensor Tracking Systems

Chen

Zhang

et al. 2023

Electronics

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Due to sensor characteristics, geographical environment, electromagnetic interference, electromagnetic silence, information countermeasures, and other reasons, there may be significant system errors in sensors in multi-sensor tracking systems, resulting in poor track-to-track association (TTTA) effect of the system. In order to solve the problem of TTTA under large system errors, this paper proposes an asynchronous anti-bias TTTA algorithm that utilizes the average distance between the nearest neighbor intervals between tracks. This algorithm proposes a systematic error interval processing method to track coordinates, and then defines the nearest neighbor interval average distance between interval coordinate datasets and interval coordinate points, and then uses grey theory to calculate the correlation degree between tracks. Finally, the Jonker–Volgenant algorithm is combined to use the canonical allocation method for TTTA judgment. The algorithm requires less prior information and does not require error registration. The simulation results show that the algorithm can ensure a high average correct association rate (over 98%) of asynchronous unequal rate tracks under large system errors, and achieve stable association, with good association and anti-bias performance. Compared with other algorithms, the algorithm maintains good performance for different target numbers and processing cycles, and has good superiority and robustness.

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ADS-BI: Compressed Indexing of ADS-B Data

Cited by 9 publications

References 23 publications

Data-Driven 4D Trajectory Prediction Model Using Attention-TCN-GRU

Data-Driven 4D Trajectory Prediction Model Using Attention-TCN-GRU

3DGraCT: A Grammar-Based Compressed Representation of 3D Trajectories

Asynchronous Anti-Bias Track-to-Track Association Algorithm Based on Nearest Neighbor Interval Average Distance for Multi-Sensor Tracking Systems

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