Nonlinear Tracking of Target Submarine Using Extended Kalman Filter (EKF)

Vikranth, S.; Sudheesh, P.; Jayakumar, M

doi:10.1007/978-981-10-2738-3_22

Cited by 11 publications

(2 citation statements)

References 13 publications

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“…Em 1960, Kalman desenvolveu uma solução recursiva para o problema de filtragem linear de dados discretos [Kalman 1960]. Seu método ficou bastante famoso e foi utilizado em diversas aplicações [Vikranth et al 2016;Patel and Thakore 2013;Seng et al 2016]. Com essa popularização o algoritmo passou a ser chamado de filtro de Kalman (FK ) em homenagem a seu criador.…”

Section: Estado Da Arteunclassified

A Machine Learning Approach to Interpolating Indoors Trajectories

Carvalho¹,

Sullivan²,

Almeida³

et al. 2021

Anais Do IX Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2021)

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In this article we propose a machine learning-based modeling to solve network overload problems caused by continuous monitoring of the trajectories of multiple tracked devices indoors. The proposed modeling was evaluated with hundreds of object coordinate locations tracked in three synthetic environments and one real environment. We show that it is possible to solve the problem of network overload increasing latency in sending data and predicting as server-side trajectories with ensemble models, such as the Random Forest, and using Artificial Neural Networks. We also show that it is possible to predict at least fifteen intermediate coordinates of the paths of the tracked objects with R2 greater than 0.95.

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Section: Estado Da Arteunclassified

A Machine Learning Approach to Interpolating Indoors Trajectories

Carvalho¹,

Sullivan²,

Almeida³

et al. 2021

Anais Do IX Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2021)

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“…The EKF uses Taylor series expansions to handle nonlinear states and observation transition functions. However, when the system is highly nonlinear, the linearization scheme will cause additional linearization errors [ 6 ]. The unscented Kalman filter (UKF) approximates the posterior probability distribution by a selected set of sigma points, which are propagated according to the unscented transformation [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Interacting Multiple Model Algorithm Based on Information-Weighted Consensus for Maneuvering Target Tracking

Ding

Liu

et al. 2018

Sensors

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Networked multiple sensors are used to solve the problem of maneuvering target tracking. To avoid the linearization of nonlinear dynamic functions, and to obtain more accurate estimates for maneuvering targets, a novel adaptive information-weighted consensus filter for maneuvering target tracking is proposed. The pseudo measurement matrix is computed with unscented transform to utilize the information form of measurements, which is necessary for consensus iterations. To improve the maneuvering target tracking accuracy and get a unified estimation in each sensor node across the entire network, the adaptive current statistical model is exploited to update the estimate, and the information-weighted consensus protocol is applied among neighboring nodes for each dynamic model. Based on posterior probabilities of multiple models, the final estimate of each sensor is acquired with weighted combination of model-conditioned estimates. Experimental results illustrate the superior performance of the proposed algorithm with respect tracking accuracy and agreement of estimates in the whole network.

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Non Linear Tracking Using Unscented Kalman Filter

Sudheesh¹,

Jayakumar²

2017

Advances in Intelligent Systems and Computing

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Nonlinear Tracking of Target Submarine Using Extended Kalman Filter (EKF)

Cited by 11 publications

References 13 publications

A Machine Learning Approach to Interpolating Indoors Trajectories

A Machine Learning Approach to Interpolating Indoors Trajectories

Adaptive Interacting Multiple Model Algorithm Based on Information-Weighted Consensus for Maneuvering Target Tracking

Non Linear Tracking Using Unscented Kalman Filter

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