Simultaneous Registration and Fusion of Multiple Dissimilar Sensors for Cooperative Driving

Li, W.; Leung, Henry

doi:10.1109/tits.2004.828169

Cited by 73 publications

(18 citation statements)

References 23 publications

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“…In [137], a multitarget detection and tracking approach for the case of multiple measurements per target and for an unknown and varying number of targets was proposed. In [138], [139], a joint sensor registration and fusion approach was developed for cooperative driving in intelligent transportation systems. In [140], [141], a multisensor and multitarget surveillance system was developed based on solving jointly the registration, data association and data fusion problems.…”

Section: ) Measurement Uncertaintymentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

286

125

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Abstract-This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.Index Terms-Intelligent vehicles, environment perception and modeling, lane and road detection, traffic sign recognition, vehicle tracking and behavior analysis, scene understanding.

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Section: ) Measurement Uncertaintymentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

286

125

View full text Add to dashboard Cite

show abstract

“…It is observed that the new covariance obtained at a time step in the general case will be lower in size compared to the covariance obtained without projection. If the estimate after the measurement-update (refer to (12)) is outside the feasible region, the same projection technique can be applied. For more detailed description, refer [14].…”

Section: State Estimation With Constraintsmentioning

confidence: 99%

“…In order to overcome the drawbacks of the R. Kandepu [4] and high order EKFs. The UKF seems to be a promising alternative for process control applications [7], [8], [12]. The UKF propagates the pdf in a simple and effective way and it is accurate up to second order in estimating mean and covariance [1].…”

Section: Introductionmentioning

confidence: 99%

Constrained state estimation using the Unscented Kalman Filter

Kandepu¹,

Imsland

Foss³

2008

2008 16th Mediterranean Conference on Control and Automation

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Abstract-A simple procedure to include state inequality constraints in the Unscented Kalman Filter is proposed. With this procedure, the information of active state constraints influences the state covariance matrix, resulting in better estimates. In a numerical example, the approach outperforms the Extended Kalman Filter implemented with constraint handling via "clipping". I. INTRODUCTIONIn the process industries one of the main goals is to make the end product at the lowest possible cost while satisfying product quality constraints. State estimation often play an important role in accomplishing this goal in process control and performance monitoring applications. There are many uncertainties to deal with in process control; model uncertainties, measurement uncertainties and uncertainties in terms of different noise sources acting on the system. In this kind of environment, representing the model state by an (approximated) probability density function (pdf) has distinct advantages. State estimation is a means to propagate the pdf of the system states over time in some optimal way. It is most common to use the Gaussian pdf to represent the model state, process and measurement noises. The Gaussian pdf can be characterized by its mean and covariance. The Kalman Filter (KF) propagates the mean and covariance of the pdf of the model state in an optimal (minimum mean square error) way in case of linear dynamic systems [5].All practical systems possess some degree of nonlinearity. Depending on the type of process and the operating region of the process, some processes can be approximated with a linear model and the KF can be used for state estimation. In some cases the linear approximation may not be accurate enough, and state estimator designs using nonlinear process models are necessary. The most common way of applying the KF to a nonlinear system is in the form of the Extended Kalman Filter (EKF). In the EKF, the pdf is propagated through a linear approximation of the system around the operating point at each time instant. In doing so, the EKF needs the Jacobian matrices which may be difficult to obtain for higher order systems, especially in the case of timecritical applications. Further, the linear approximation of the system at a given time instant may introduce errors in the state which may lead the state to diverge over time. In other words, the linear approximation may not be appropriate for some systems. In order to overcome the drawbacks of the

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“…The spatial bias [1] originates from the stationary bias of each sensor and the measurement bias of range and angle, etc. The temporal bias [2] originates from asynchronous measurement instant of each sensor and the time differences among sensors are unknown. The fusion centers employ corresponding scenario to fuse the information of different sensor.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Registration for Multi-sensor Fusion Systems

Bu¹,

Zhou²

2017

dtcse

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Abstract.A method of online sensor fusion with spatially and temporally misaligned sensors is considered in this paper. A spatiotemporal registration mode is developed for sensor alignment, and augment the target state vector with spatiotemporal bias. An unscented Kalman filter (UKF) is used to fuse and register these sensors, then the target state estimation as well as the spatial and temporal bias estimation can be obtained simultaneously. Simulations show that the proposed algorithm not only can align these sensors properly with both spatial and temporal bias, but can also obtain accurate fused tracks.

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Simultaneous Registration and Fusion of Multiple Dissimilar Sensors for Cooperative Driving

Cited by 73 publications

References 23 publications

Overview of Environment Perception for Intelligent Vehicles

Overview of Environment Perception for Intelligent Vehicles

Constrained state estimation using the Unscented Kalman Filter

Spatiotemporal Registration for Multi-sensor Fusion Systems

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