A New Outlier-Robust Student's t Based Gaussian Approximate Filter for Cooperative Localization

Huang, Yulong; Zhang, Yonggang; Xu, Bo; Wu, Zhen; Chambers, Jonathon A.

doi:10.1109/tmech.2017.2744651

Cited by 151 publications

(63 citation statements)

References 24 publications

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“…In order to estimate the position of a target AUV, its relative position with respect to the sensor AUVs is measured using acoustic waves with the help of techniques mentioned in Section 2. Once the relative position of a target w.r.t the accurate sensors' positions is known, its position can be estimated using different estimation algorithms proposed in the research literature [1,[42][43][44][45][46]. However, measurement of this relative position in water is a challenging task due to inconsistent and unpredictable underwater environment.…”

Section: Background Theory and Problem Statementmentioning

confidence: 99%

A Review on Optimal Placement of Sensors for Cooperative Localization of AUVs

Razzaqi

Farid

2019

Journal of Sensors

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Self-positioning of submerged Autonomous Underwater Vehicles (AUVs) is a challenging task due to nonavailability of GPS signals. One of the most recent solutions for this is the use of surface vehicles (sensors) for cooperative localization of the underwater vehicles (targets) by measuring their relative positions. However, correct placement of the surface sensors is very critical as their geometric configuration affects their observability and hence availability of their relative positions information to the targets. In this paper, a comparative survey of sensors’ optimal formation techniques for cooperative localization of AUVs has been presented. Introduction to the basic cooperative localization techniques and background theory of optimal sensor placements have been provided. This paper can also serve as a fundamental reading material for students and researchers pursuing research on optimal sensor placement for cooperative localization.

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Section: Background Theory and Problem Statementmentioning

confidence: 99%

A Review on Optimal Placement of Sensors for Cooperative Localization of AUVs

Razzaqi

Farid

2019

Journal of Sensors

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“…One of the main problems of the KF-based filters is that in the case of modeling and parameter uncertainties, the performance of estimation is degraded. For example, state noises and measurement noises have heavy-tailed and/or skewed non-Gaussian distributions when measurements have much clutter [15][16][17]. To deal with this unknown noises, some robust methods have been presented [18,19], when the system with a heavy-tailed or a skewed measurement noises, robust Kalman filters have been proposed by employing the Student's t or skew-t distribution to model measurement noises [15,20].…”

Section: Introductionmentioning

confidence: 99%

A Novel Smooth Variable Structure Smoother for Robust Estimation

Chen

Yan

et al. 2020

Sensors

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The smooth variable structure filter (SVSF) is a new-type filter based on the sliding-mode concepts and has good stability and robustness in overcoming the modeling uncertainties and errors. However, SVSF is insufficient to suppress Gaussian noise. A novel smooth variable structure smoother (SVSS) based on SVSF is presented here, which mainly focuses on this drawback and improves the SVSF estimation accuracy of the system. The estimation of the linear Gaussian system state based on SVSS is divided into two steps: Firstly, the SVSF state estimate and covariance are computed during the forward pass in time. Then, the smoothed state estimate is computed during the backward pass by using the innovation of the measured values and covariance estimate matrix. According to the simulation results with respect to the maneuvering target tracking, SVSS has a better performance compared with another smoother based on SVSF and the Kalman smoother in different tracking scenarios. Therefore, the SVSS proposed in this paper could be widely applied in the field of state estimation in dynamic system.

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“…Based on the Bayesian framework, Huang et al [27] proposed a robust Gaussian approximate fixed interval smoother aiming at nonlinear systems with heavy-tailed process and measurement noises. Paper [28] proposed a new outlier-robust Student's t based Gaussian approximate filter aiming at the outlier measurements of velocity and range in cooperative localization of autonomous underwater vehicles. However, the computation load of the Bayesian and maximum-likelihood methods is so complex that their practical applications are dramatically constrained.…”

Section: Introductionmentioning

confidence: 99%

An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer

et al. 2019

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The strap-down inertial navigation system (SINS) is a commonly used sensor for autonomous underground navigation, which can be used for shearer positioning under a coal mine. During the process of initial alignment, inaccurate or time-varying noise covariance matrices will significantly degrade the accuracy of the initial alignment of the shearer. To overcome the performance degradation of the existing initial alignment algorithm under complex underground environment, a novel adaptive filtering algorithm is proposed by the integration of the strong tracking Kalman filter and the sequential filter for the initial alignment of the shearer with complex underground environment. Compared with the traditional multiple fading factor strong tracking Kalman filter (MSTKF) method, the proposed MSTSKF algorithm integrates the advantage of strong tracking Kalman filter and sequential filter, and multiple fading factor and forgetting factor for east and north velocity measurement are designed in the algorithm, respectively, which can effectively weaken the coupling relationship between the different states and increase strong robustness against process uncertainties. The simulation and experiment results show that the proposed MSTSKF method has better initial alignment accuracy and robustness than existing strong tracking Kalman filter algorithm.

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A New Outlier-Robust Student's t Based Gaussian Approximate Filter for Cooperative Localization

Cited by 151 publications

References 24 publications

A Review on Optimal Placement of Sensors for Cooperative Localization of AUVs

A Review on Optimal Placement of Sensors for Cooperative Localization of AUVs

A Novel Smooth Variable Structure Smoother for Robust Estimation

An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer

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