Multisensor Fusion Algorithms for Maneuvering Target Tracking

Fong, Li-Wei; Fan, Chan-Yu

doi:10.1109/icelie.2006.347216

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…The SSP is simply the square of the L 2 norm of the d-dimensional vector w introduced in Eq. (10). The hyperparameters are updated as…”

Section: B Bayesian Regularizationmentioning

confidence: 99%

“…In the literature, [7] and [10] proposed using ANNs to determine the weights for linearly combining sensor state estimates. More recently, we proposed learning-based nonlinear fusion [4], [20]; the main contribution of this paper is to further investigate the ANN-based fusers, accounting for both communication and computation constraints in long-haul sensor networks and their effects on fusion performance.…”

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confidence: 99%

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Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

Liu

Brigham

Rao

2017

IEEE Trans. on Signal and Inf. Process. over Networks

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Nageswara S. V. (2017) 'Estimation and fusion for tracking over long-haul links using articial neural networks.', IEEE transactions on signal and information processing over networks., 3 (4). pp. 760-770. Further information on publisher's website: Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics -such as the position -is expected to possess much improved quality. In this work, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). The joint effect of (i) imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.Index Terms-Long-haul sensor networks, state estimate fusion, artificial neural networks, estimation bias, error regularization, root-mean-square-error (RMSE) performance, reporting deadline.

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“…The SSP is simply the square of the L 2 norm of the d-dimensional vector w introduced in Eq. (10). The hyperparameters are updated as…”

Section: B Bayesian Regularizationmentioning

confidence: 99%

mentioning

confidence: 99%

Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

Liu

Brigham

Rao

2017

IEEE Trans. on Signal and Inf. Process. over Networks

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“…Artificial Neural Network (ANN) is an algorithmic mathematical model for distributed parallel information processing which imitates the characteristics of information transmission and reflective behavior of human brain nervous system [14]. Due to the good approximation performance of ANN, it has been well applied in the state estimation and information fusion [15]- [21]. Rao et al proposed various learning-based estimators to solve the fusion estimation problem (i.e., Artificial Neural Networks (ANNs), the Nadaraya-Watson estimator and the Nearest Neighbor Projective Fuser) [22]- [24].…”

Section: Introductionmentioning

confidence: 99%

The Research on Distributed Fusion Estimation Based on Machine Learning

Peng

Hao

2020

IEEE Access

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Multi-sensor distributed fusion estimation algorithms based on machine learning are proposed in this paper. Firstly, using local estimations as inputs and estimations of three classic distributed fusion (weighted by matrices, by diagonal matrices and by scalars) as the training sets, three distributed fusion algorithms based on BP network (BP net-based fusion weighted by matrices, by diagonal matrices and by scalar) are proposed and the selection basis of the number of nodes in hidden layer is given. Furthermore, by using local estimations as inputs and centralized fusion estimation as training set, another recurrent netbased distributed fusion algorithm is proposed, in the case that neither true states nor cross-covariance matrices is available. This method is not limited to the linear minimum variance (LMV) criterion, so its accuracy is higher than the classical three distributed fusion algorithms. A radar tracking simulation verifies the effectiveness of the proposed fusion networks. INDEX TERMS Distributed fusion, machine learning, recurrent networks, BP network.

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“…al. [13] propose using ANNs for sensor fusion, where the neural networks are used to determine the weights for linearly combining sensor state estimates. We further explore ANNs for nonlinear sensor fusion.…”

Section: Introductionmentioning

confidence: 99%

On state fusers over long-haul sensor networks

Brigham

Kumar

Rao

et al. 2012

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

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We consider a network of sensors wherein the state estimates are sent from sensors to a fusion center to generate a global state estimate. The underlying fusion algorithm affects the performance measure QCC (τ ) (with subscripts CC indicating the effects of the communications and computing quality) of the global state estimate computed within the allocated time τ . We present a probabilistic performance bound on QCC (τ ) as a function of the distributions of state estimates, communications parameters as well as the fusion algorithm. We present simulations of simplified scenarios to illustrate the qualitative effects of different fusers, and system-level simulations to complement the analytical results.

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Multisensor Fusion Algorithms for Maneuvering Target Tracking

Cited by 5 publications

References 7 publications

Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

The Research on Distributed Fusion Estimation Based on Machine Learning

On state fusers over long-haul sensor networks

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