Fusion estimation for multi-sensor networked systems with packet loss compensation

Ding, Juntang; Sun, Shuli; Ma, Jan; Li, Na

doi:10.1016/j.inffus.2018.01.008

Cited by 64 publications

(18 citation statements)

References 32 publications

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“…A new fusion method is introduced in [50] for multi-sensor systems with packet loss of measurements. For this aim, centralized estimators, consisting of a predictor, a filter, and a soother, are developed in a linear unbiased minimum variance (LUMV) sense.…”

Section: A Literature Reviewmentioning

confidence: 99%

A New Fusion Estimation Method for Multi-Rate Multi-Sensor Systems With Missing Measurements

et al. 2020

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A new fusion strategy is introduced in this article to estimate state for multi-rate multisensor systems with missing measurements. N sensors, which possess various sampling rates, render the measurements. Missing measurements with a certain probability pattern are also investigated. For these types of systems, Multi-rate Kalman filters are designed to estimate a target position at various sampling rates. Next, Ordered Weighted Averaging (OWA) operator is utilized to integrate multi-rate Kalman filters and improve the estimation quality. A new fusion strategy based on a real covariance matrix is introduced for updating the weighting factors, and proof of convergence is granted. Simulation studies on a tracking system verify the superior performance of the proposed fusion strategy in comparison with the Kalman filter, the multi-rate Kalman filters, and also the previous fusion methodology. INDEX TERMS Multi-rate Kalman filter, estimation, sensor data fusion, missing measurements, OWA operator.

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Section: A Literature Reviewmentioning

confidence: 99%

A New Fusion Estimation Method for Multi-Rate Multi-Sensor Systems With Missing Measurements

et al. 2020

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“…To make scheduling open to some particular cases, the work of Schmidt [47] handled a situation where a message instance is followed by the transmission of additional CAN frames. Alternatively, several information‐fusion‐based IVNCS delay‐compensation approaches have been reviewed in [48]. Unlike the conventional event‐triggering CAN communication, the network imperfections have been avoided by a pipeline device with a deterministic data transmission [49].…”

Section: Requirements Of Tomorrow's Vehicles and Related Work About Cmentioning

confidence: 99%

Controller area network reliability: overview of design challenges and safety related perspectives of future transportation systems

Lakhal

Nasri

Adouane

et al. 2020

IET intell. transp. syst

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The in‐vehicular networked control system is among the most critical embedded processes. The controller area network (CAN) has prevailed intra‐vehicle communication for decades. Meanwhile, requirements of future transportation systems are expected to emphasise the in‐vehicle communication complexity, which endangers the reliability/safety of the intelligent navigation. At first, this study reviews the recent solutions proposed to overcome the CAN expanding complexity. Challenges that tomorrow's intelligent vehicles may raise for CAN reliability are investigated. The comprehensive coverage of current research efforts to remove the impact of these challenges is presented. Further, the in‐vehicle system reliability of future automated vehicles is also related to the fault diagnosis performances. Hence, different classes of system‐level diagnosis strategies are compared relatively to the requirements of automotive embedded networks. Furthermore, to thoroughly cover CAN reliability engineering issues, focus is given to the automotive validation techniques. The hardware in the loop, real‐time analysis and computer‐aided‐design tools intervene in various phases along the in‐vehicular network life cycle. Parameters that stand behind the efficiency and accuracy of these techniques in validating the new generation of vehicles are analysed. The authors finally draw some deductive predictions about the future directions related to the reliability of the intelligent transportation system in‐vehicular communication.

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“…In [16] and [24], based on the same compensation strategy, delayed measurements, finite-step correlated noises and packet loss are considered, respectively. In [25], fusion algorithms for systems with multi-sensor measurement are considered in which different sensors have different packet loss rates.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Recursive Filter for Nonlinear Systems with Finite-step Correlated Noises and Packet Dropout Compensations

Tan

Cheng²,

Wang

et al. 2020

Measurement Science Review

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This paper is focused on the nonlinear state estimation problem with finite-step correlated noises and packet loss. Firstly, by using the projection theorem repeatedly, the mean and covariance of process noise and measurement noise in the condition of measurements before the current epoch are calculated. Then, based on the Gaussian approximation recursive filter (GASF) and the prediction compensation mechanism, one-step predictor and filter with packet dropouts are derived, respectively. Based on these, a nonlinear Gaussian recursive filter is proposed. Subsequently, the numerical implementation is derived based on the cubature Kalman filter (CKF), which is suitable for general nonlinear system and with higher accuracy compared to the algorithm expanded from linear system to nonlinear system through Taylor series expansion. Finally, the strong nonlinearity model is used to show the superiority of the proposed algorithm.

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Fusion estimation for multi-sensor networked systems with packet loss compensation

Cited by 64 publications

References 32 publications

A New Fusion Estimation Method for Multi-Rate Multi-Sensor Systems With Missing Measurements

A New Fusion Estimation Method for Multi-Rate Multi-Sensor Systems With Missing Measurements

Controller area network reliability: overview of design challenges and safety related perspectives of future transportation systems

Gaussian Recursive Filter for Nonlinear Systems with Finite-step Correlated Noises and Packet Dropout Compensations

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