Underwater Vehicle Localization with Complementary Filter: Performance Analysis in the Shallow Water Environment

Vasilijević, Antonio; Borovic, B.; Vukić, Zoran

doi:10.1007/s10846-012-9766-6

Cited by 38 publications

(17 citation statements)

References 19 publications

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“…where E[·] represents the expectation operation, and ϕ is an arbitrary element of Θ, and Θ (−ϕ) is the set of all elements in Θ except for ϕ, and c ϕ denotes the constant with respect to variable ϕ. Since the variational parameters are coupled, we need to employ fixed-point iterations to solve (12), where the approximate posterior PDF q(ϕ) of the arbitrary element ϕ is updated as q (i+1) (ϕ) at the i + 1th iteration using the approximate posterior PDF q (i) (Θ (−ϕ) ) [24]. The iterations converge to a local optimum of (12).…”

Section: A New Outlier-robust Student's T Based Ga Filtermentioning

confidence: 99%

“…In practical cooperative localization, outlier measurements of velocity and range may occur, which can induce heavy-tailed non-Gaussian process and measurement noises respectively. The outlier measurements of velocity occur when the Doppler velocity log (DVL) is physically misaligned with the body framework or when water lock occurs [12]. The outlier measurements of range are predominantly caused by the multiple acoustic propagation paths between source and receiver that are induced by the refraction of sound waves due to changes in sound speed with depth and reflections off the surface and sea bed [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Huang

Zhang

et al. 2017

IEEE/ASME Trans. Mechatron.

142

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In this paper, a new outlier-robust Student's t based Gaussian approximate filter is proposed to address the heavytailed process and measurement noises induced by the outlier measurements of velocity and range in cooperative localization of autonomous underwater vehicles (AUVs). The state vector, scale matrices and degrees of freedom (dof) parameters are estimated based on the variational Bayesian approach by using the constructed Student's t based hierarchical Gaussian statespace model. The performances of the proposed filter and existing filters are tested in the cooperative localization of an AUV through a lake trial. Experimental results illustrate that the proposed filter has better localization accuracy and robustness than existing state-of-the-art outlier-robust filters.

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Section: A New Outlier-robust Student's T Based Ga Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Huang

Zhang

et al. 2017

IEEE/ASME Trans. Mechatron.

142

View full text Add to dashboard Cite

show abstract

“…In these situations, a single ping by an acoustic beacon can be detected as a complex sequence of arrivals [1]. The work in [31] about underwater vehicle localization provides further examples of measurements from USBL and DVL susceptible to successive outliers especially in shallow waters. In such a case, numerical tests have confirmed a better performance of the LEL filter as compared with the rLS.AO.…”

Section: Synthetic Data: Comparison With An Alternative Robust Kalmanmentioning

confidence: 99%

Output outlier robust state estimation

Palma

Indiveri

2016

Adaptive Control & Signal

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This work addresses state estimation in presence of outliers in observed data. Outlying data and measurements have a most relevant impact in many control and signal processing applications including marine systems related ones: underwater navigation systems exploiting acoustic data, for example, are frequently affected by outlying measurements. Other on-board sensors and devices are likely to produce measurements contaminated by outlier because of the harsh operating conditions of marine systems. Given the general interest for dealing with measurement outliers in a number of applications, this paper describes a state estimation solution exhibiting robustness to output outliers. The system model is assumed to be linear (either time varying or time invariant) discrete time. The proposed observer is designed by extending an outlier robust static parameter identification algorithm to the case of a linear dynamic plant. The designed estimator has a predictor/corrector structure like the Kalman filter and the Luenberger observer. Simulation and experimental results are provided illustrating the robustness of the derived solution to measurement outliers as compared with the Kalman filter. The proposed solution is also compared with alternative outlier robust state estimation filters showing its effectiveness, in particular, in the presence of measurements outliers occurring in a consecutive sequence. Because of its deterministic execution time and limited numerical complexity, the proposed state estimator can be readily applied in real-time applications. Figure 4. Noisy output with outliers (depicted as blue stars, D 0:2) and non-outliers in grey generated through the model in (41). [Colour figure can be viewed at wileyonlinelibrary.com] The LEL Filter column refers to the interval of singular values over 3000 Monte Carlo runs assuming outliers generated through the model in (41). LEL, least entropy like.with synthetic and real data have been discussed showing the potentiality of the proposed filter as compared with the standard KF. Comparison with alternative robust filtering approaches have shown its robustness to sequences of successive measurement outliers as well. The proposed robust LEL filter may be exploited in real-time applications because of its limited and predictable (i.e. deterministic) computational effort.

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“…Accurate positioning of surface and airborne vehicles such as ships and airplanes is possible using Global Navigation Satellite Systems (GNSS) such as the Global Positioning System (GPS) (Grenon et al, 2001). However, since GPS signals do not penetrate beneath the water, this system cannot be used in underwater vehicles (Yun et al, 1999; Vasilijevic et al, 2012). For this reason, Inertial Navigation Systems (INS) are utilised as an alternative solution in many underwater applications (Titterton and Weston, 2004).…”

Section: Introductionmentioning

confidence: 99%

A Low Complexity Integrated Navigation System for Underwater Vehicles

Emami

Taban

2018

J. Navigation

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This paper proposes a simplified algorithm for reducing the computational load of the conventional underwater integrated navigation system. The system usually comprises a three-dimensional accelerometer, a three-dimensional gyroscope, a three-dimensional Doppler Velocity Log (DVL) and a data fusion algorithm, such as a Kalman Filter (KF). Since the expected variations of roll, pitch and depth are small, these quantities are assumed to be constant, and the proposed system is designed in a two-dimensional form. Due to the low speed of the vehicle, the nonlinear dynamic equation of the velocity can be simplified in a linear form. We also simplify the conventional KF in order to avoid matrix multiplications and matrix inversions. The performance of the designed system is evaluated in a sea trial by an Autonomous Underwater Vehicle (AUV). The results show that the proposed system can significantly reduce the computational load of the conventional integrated navigation system without a significant reduction in position and velocity accuracy.

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Underwater Vehicle Localization with Complementary Filter: Performance Analysis in the Shallow Water Environment

Cited by 38 publications

References 19 publications

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

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

Output outlier robust state estimation

A Low Complexity Integrated Navigation System for Underwater Vehicles

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