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Multi-sensor fusion with interacting multiple model filter for improved aircraft position accuracy.

Cho T, Lee C, Choi S - Sensors (Basel) (2013)

Bottom Line: The International Civil Aviation Organization (ICAO) has decided to adopt Communications, Navigation, and Surveillance/Air Traffic Management (CNS/ATM) as the 21st century standard for navigation.Accordingly, ICAO members have provided an impetus to develop related technology and build sufficient infrastructure.These sensors can track aircraft positions more accurately than existing radar and can compensate for the blind spots in aircraft surveillance.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronics Engineering, Inha University, Incheon city 402-751, Korea. burujo@naver.com

ABSTRACT
The International Civil Aviation Organization (ICAO) has decided to adopt Communications, Navigation, and Surveillance/Air Traffic Management (CNS/ATM) as the 21st century standard for navigation. Accordingly, ICAO members have provided an impetus to develop related technology and build sufficient infrastructure. For aviation surveillance with CNS/ATM, Ground-Based Augmentation System (GBAS), Automatic Dependent Surveillance-Broadcast (ADS-B), multilateration (MLAT) and wide-area multilateration (WAM) systems are being established. These sensors can track aircraft positions more accurately than existing radar and can compensate for the blind spots in aircraft surveillance. In this paper, we applied a novel sensor fusion method with Interacting Multiple Model (IMM) filter to GBAS, ADS-B, MLAT, and WAM data in order to improve the reliability of the aircraft position. Results of performance analysis show that the position accuracy is improved by the proposed sensor fusion method with the IMM filter.

No MeSH data available.


Related in: MedlinePlus

Proposed sensor fusion method with the IMM filter.
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f3-sensors-13-04122: Proposed sensor fusion method with the IMM filter.

Mentions: Through the mixing process of the IMM filter, measurements from the N sensors are converted into estimates and at t = k −1. The prediction process applied to these values afford the estimates and at t = k. Further, through the process of updating, we obtain and . Finally, through the process of combining each sensor data, we obtain the final estimates x̂1, x̂2 ⋯, x̂N and covariances P1, P2 ⋯, PN. The resulting values are sent to the main filter. In the main filter, the final results are obtained using the following equations:(13)x^=P[P1−1x^1+P2−1x^2+⋯+PN−1x^N](14)P=[P1−1+P2−1+⋯+PN−1]−1x̂1, x̂2 ⋯, x̂N comprise the final estimate calculated by the sub-filters; and P1, P2 ⋯, PN are the covariances calculated by the sub-filters. x̂ and P are the final results obtained from the main filter. The proposed sensor fusion method is shown in Figure 3.


Multi-sensor fusion with interacting multiple model filter for improved aircraft position accuracy.

Cho T, Lee C, Choi S - Sensors (Basel) (2013)

Proposed sensor fusion method with the IMM filter.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3673074&req=5

f3-sensors-13-04122: Proposed sensor fusion method with the IMM filter.
Mentions: Through the mixing process of the IMM filter, measurements from the N sensors are converted into estimates and at t = k −1. The prediction process applied to these values afford the estimates and at t = k. Further, through the process of updating, we obtain and . Finally, through the process of combining each sensor data, we obtain the final estimates x̂1, x̂2 ⋯, x̂N and covariances P1, P2 ⋯, PN. The resulting values are sent to the main filter. In the main filter, the final results are obtained using the following equations:(13)x^=P[P1−1x^1+P2−1x^2+⋯+PN−1x^N](14)P=[P1−1+P2−1+⋯+PN−1]−1x̂1, x̂2 ⋯, x̂N comprise the final estimate calculated by the sub-filters; and P1, P2 ⋯, PN are the covariances calculated by the sub-filters. x̂ and P are the final results obtained from the main filter. The proposed sensor fusion method is shown in Figure 3.

Bottom Line: The International Civil Aviation Organization (ICAO) has decided to adopt Communications, Navigation, and Surveillance/Air Traffic Management (CNS/ATM) as the 21st century standard for navigation.Accordingly, ICAO members have provided an impetus to develop related technology and build sufficient infrastructure.These sensors can track aircraft positions more accurately than existing radar and can compensate for the blind spots in aircraft surveillance.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronics Engineering, Inha University, Incheon city 402-751, Korea. burujo@naver.com

ABSTRACT
The International Civil Aviation Organization (ICAO) has decided to adopt Communications, Navigation, and Surveillance/Air Traffic Management (CNS/ATM) as the 21st century standard for navigation. Accordingly, ICAO members have provided an impetus to develop related technology and build sufficient infrastructure. For aviation surveillance with CNS/ATM, Ground-Based Augmentation System (GBAS), Automatic Dependent Surveillance-Broadcast (ADS-B), multilateration (MLAT) and wide-area multilateration (WAM) systems are being established. These sensors can track aircraft positions more accurately than existing radar and can compensate for the blind spots in aircraft surveillance. In this paper, we applied a novel sensor fusion method with Interacting Multiple Model (IMM) filter to GBAS, ADS-B, MLAT, and WAM data in order to improve the reliability of the aircraft position. Results of performance analysis show that the position accuracy is improved by the proposed sensor fusion method with the IMM filter.

No MeSH data available.


Related in: MedlinePlus