Multi-Sensor Fusion with Interacting Multiple Model Filter for Improved Aircraft Position Accuracy

Cho, Taehwan; Lee, Chang-Ho; Choi, Sang-Bang

doi:10.3390/s130404122

Cited by 33 publications

(19 citation statements)

References 17 publications

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“…[1,4] talk about several multi-sensor data fusion methods. [5] and [6] are also the same but latest attempts. In [1], the computational complexities of these methods are compared based on mathematical formulation, but no simulation or implementation proof is presented.…”

Section: Introductionmentioning

confidence: 91%

Computational Complexity Comparison of Multi-Sensor Single Target Data Fusion Methods by MATLAB

Hoseini¹,

Ashraf²

2013

IJCCMS

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Section: Introductionmentioning

confidence: 91%

Computational Complexity Comparison of Multi-Sensor Single Target Data Fusion Methods by MATLAB

Hoseini¹,

Ashraf²

2013

IJCCMS

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“…However, in scenarios where the accuracy of the inertial measurement units in the SINS is low, the aircraft manoeuvre will damage the accuracy of the SINS, thereby, reducing the failure detection sensitivity. The interacting multiple model (IMM) estimation algorithms [21,22,23] are, therefore, designed to overcome this difficulty by incorporating a set of candidate models to represent different uncertainties under realistic conditions. Research on the fault detection and isolation problem with the IMM algorithm has attracted a recent surge of interest [24,25,26,27,28,29].…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter

Yang

Mohammadi

Chen

2016

Sensors

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Motivated by the key importance of multi-sensor information fusion algorithms in the state-of-the-art integrated navigation systems due to recent advancements in sensor technologies, telecommunication, and navigation systems, the paper proposes an improved and innovative fault-tolerant fusion framework. An integrated navigation system is considered consisting of four sensory sub-systems, i.e., Strap-down Inertial Navigation System (SINS), Global Navigation System (GPS), the Bei-Dou2 (BD2) and Celestial Navigation System (CNS) navigation sensors. In such multi-sensor applications, on the one hand, the design of an efficient fusion methodology is extremely constrained specially when no information regarding the system’s error characteristics is available. On the other hand, the development of an accurate fault detection and integrity monitoring solution is both challenging and critical. The paper addresses the sensitivity issues of conventional fault detection solutions and the unavailability of a precisely known system model by jointly designing fault detection and information fusion algorithms. In particular, by using ideas from Interacting Multiple Model (IMM) filters, the uncertainty of the system will be adjusted adaptively by model probabilities and using the proposed fuzzy-based fusion framework. The paper also addresses the problem of using corrupted measurements for fault detection purposes by designing a two state propagator chi-square test jointly with the fusion algorithm. Two IMM predictors, running in parallel, are used and alternatively reactivated based on the received information form the fusion filter to increase the reliability and accuracy of the proposed detection solution. With the combination of the IMM and the proposed fusion method, we increase the failure sensitivity of the detection system and, thereby, significantly increase the overall reliability and accuracy of the integrated navigation system. Simulation results indicate that the proposed fault tolerant fusion framework provides superior performance over its traditional counterparts.

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“…A tag has an identification information and memory that stores additional data. The reader is able to read and write the data from tags [12] [14]. Each tag is equipped with wireless module which can transmit data to the reader.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Hybrid Data Fusion Based on Probabilistic Detection Identification Model for Intelligent Rail Communication Highway

Patgar¹,

Shankaraiah²

2016

IJSACS

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Data fusion has an emerging technology that received core attraction for maneuvering and non-maneuvering application. The multiple sources in the data fusion process are distributed accuracy based systems that differ from traditional single source in many ways; these have suppressed the irrelevant data and noise, better numerical precision, reliable, stable and accurate. The end-to-end basic probabilistic models have been proposed for different architectures in data fusion to manage sensor information. This paper proposes probabilistic detection identification model based on Bayes theorem and compare its performance with traditional single source network. Subsequently, it is cross validated using various data folds of kinematics measurements available from Differential GPS, Wireless Sensor and Radio Frequency Identification. This study proves the probabilistic model offers significant detection performance accuracy level of 95% for moving locomotives across wide range of operational scenarios.

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Multi-Sensor Fusion with Interacting Multiple Model Filter for Improved Aircraft Position Accuracy

Cited by 33 publications

References 17 publications

Computational Complexity Comparison of Multi-Sensor Single Target Data Fusion Methods by MATLAB

Computational Complexity Comparison of Multi-Sensor Single Target Data Fusion Methods by MATLAB

Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter

The Impact of Hybrid Data Fusion Based on Probabilistic Detection Identification Model for Intelligent Rail Communication Highway

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