Covariance intersection-based sensor fusion for sounding rocket tracking and impact area prediction

Ferreira, Julio Cesar Bolzani de Campos; Waldmann, Jacques

doi:10.1016/j.conengprac.2006.07.002

Cited by 35 publications

(13 citation statements)

References 21 publications

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“…Furthermore, a comparative analysis of CI with different optimal fusion rules is presented in Reference [ 98 ]. The CI method is applied in many applications, namely, localization [ 110 , 111 , 112 ], target tracking [ 113 , 114 ], simultaneous localization and mapping (SLAM) [ 1 , 2 ], image integration [ 99 ], NASA MARS rover [ 101 ] and spacecraft state estimation [ 114 , 115 ].…”

Section: Fusion Under Unknown Correlationmentioning

confidence: 99%

Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency

Bakr

Lee

2017

Sensors

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The paradigm of multisensor data fusion has been evolved from a centralized architecture to a decentralized or distributed architecture along with the advancement in sensor and communication technologies. These days, distributed state estimation and data fusion has been widely explored in diverse fields of engineering and control due to its superior performance over the centralized one in terms of flexibility, robustness to failure and cost effectiveness in infrastructure and communication. However, distributed multisensor data fusion is not without technical challenges to overcome: namely, dealing with cross-correlation and inconsistency among state estimates and sensor data. In this paper, we review the key theories and methodologies of distributed multisensor data fusion available to date with a specific focus on handling unknown correlation and data inconsistency. We aim at providing readers with a unifying view out of individual theories and methodologies by presenting a formal analysis of their implications. Finally, several directions of future research are highlighted.

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Section: Fusion Under Unknown Correlationmentioning

confidence: 99%

Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency

Bakr

Lee

2017

Sensors

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“…Although the nonlinear model precisely describes a motion of SLV, it needs complex prior information concerning a SLV flight environment. In case of the linear dynamic model, on the other hand, a constant acceleration (CA) model with multiple hypotheses which takes advantage of Singer's model [28] is introduced in [29]. In [29], to describe motion of a sounding rocket using the CA model, the rocket motion is separated into two parts, propelled flight and free fall flight phase by utilizing empirically tuned, independent probability density function.…”

Section: Experimental Analysis Of Fusion Predictorsmentioning

confidence: 99%

Multisensory Prediction Fusion of Nonlinear Functions of the State Vector in Discrete-Time Systems

Song

Shin

2015

International Journal of Distributed Sensor Networks

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We propose two new multisensory fusion predictors for an arbitrary nonlinear function of the state vector in a discrete-time linear dynamic system. Nonlinear function of the state (NFS) represents a nonlinear multivariate functional of state variables, which can indicate useful information of the target system for automatic control. To estimate the NFS using multisensory information, we propose centralized and decentralized predictors. For multivariate polynomial NFS, we propose an effective closed-form computation procedure for the predictor design. For general NFS, the most popular procedure for the predictor design is based on the unscented transformation. We demonstrate the effectiveness and estimation accuracy of the fusion predictors on theoretical and numerical examples in multisensory environment.

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“…Bolzani de Campos Ferreira et al [18] investigated the use of CI for the fusion of data from a pair of distinct radar sites at Alcantara Launch Centre (ALC) to track a sounding rocket and to predict the impact point and its uncertainty area on the ground in compliance with safety-of-flight issues. Debiased measurement transformation from spherical to cartesian coordinates, boost and free-fall models are embedded in the Kalman filters.…”

Section: Prior Work In the Fieldmentioning

confidence: 99%

“…This appendix gives a proof that the CI yields a consistent estimate for any value ofP ab and ω providing that a and b are consistent [24]. The CI algorithm calculates its mean using the equation (18). So, the actual error in this estimate is c = P cc {ωP −1 aaã + (1 − ω)P −1 bbb } By taking the outer products and expectations, the actual mean squared error, which is committed by using the equation (18) to calculate the mean, is…”

Section: Appendix 2 a Proof Of Consistencymentioning

confidence: 99%

Unmanned aerial vehicle navigation and mapping

Lazarus

Tsourdos

Silson

et al. 2008

Proceedings of the Institution of Mechanical Engineers, Part G:

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The unmanned aerial vehicle (UAV) has significant advantages over the ground vehicle, where it can achieve a high degree of manoeuvrability, high speed response time and the ability of large area coverage. The problem considered here is that of an UAV localization and mapping using an extended Kalman filter (EKF), interval analysis (IA), covariance intersection (CI) and Hough transform (HT) for a partially known environment. The map is known partially in the sense that the obstacles and the land-marks are known to some extent. The vehicle is localized with respect to the known obstacles and to recognise the unknown obstacles to update the map of the environment. The focus is to develop an approach which can give a guaranteed performance of sensor-based localization and mapping that would increase the safety of the aerial vehicle and to produce a better performance in building the map of the environment. The guaranteed performance is quantified by explicit bounds of the position estimate of the vehicle. Generally, the UAVs carry the required sensors such as inertial sensors, accelerometers, and gyroscopes, to measure the acceleration and the angular rate, while the obstacle detection and the mapmaking is carried out with time of flight sensors such as ultrasonic or laser sensors. Most of these sensors give overlapping or complementary information, which offers scope for exploiting data fusion. This task of data fusion is accomplished by combining the measurements from different sensors that are obtained from two different methods namely EKF and IA, and by processing these measurements with a data fusion algorithm using the CI principle. This fused information is used with the measurements from the time of flight sensors such as laser sensor to update the map of the environment by applying the HT technique. The algorithms are complementary in the sense that they compensate for each other's limitations, so that the resulting performance of the sensor system is better than of its individual components, which in turn, improves the accuracy and richness in updating the map of the partially known environment. This proposed intelligent sensor system can provide a mathematically provable performance guarantees that are achievable in practice.

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Covariance intersection-based sensor fusion for sounding rocket tracking and impact area prediction

Cited by 35 publications

References 21 publications

Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency

Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency

Multisensory Prediction Fusion of Nonlinear Functions of the State Vector in Discrete-Time Systems

Unmanned aerial vehicle navigation and mapping

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