Improvements to Terrain Aided Navigation Accuracy in Deep-Sea Space by High Precision Particle Filter initialization

Wang, Rupeng; Li, Ye; Ma, Teng; Zheng, Cong; Yan, Gao; Xu, Pengfei

doi:10.1109/access.2019.2960563

Cited by 12 publications

(3 citation statements)

References 19 publications

(24 reference statements)

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“…where μ represents the mean of the prediction error and N is the number of errors. In order to express the actual size of the prediction error over a long period of time and reduce the likelihood of significant errors, we employed the confidence interval method [48,49]. We extracted the sequence of prediction errors from the trained model, set the confidence level to 95%, and solved the confidence interval as ( μ − zσ, μ + zσ).…”

Section: Algorithm 1 Analysis Process For Normal Distributionmentioning

confidence: 99%

Data-Driven Fault Detection of AUV Rudder System: A Mixture Model Approach

Zhang

Zhang²,

Yan

et al. 2023

Machines

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Based on data-driven and mixed models, this study proposes a fault detection method for autonomous underwater vehicle (AUV) rudder systems. The proposed method can effectively detect faults in the absence of angle feedback from the rudder. Considering the parameter uncertainty of the AUV motion model resulting from the dynamics analysis method, we present a parameter identification method based on the recurrent neural network (RNN). Prior to identification, singular value decomposition (SVD) was chosen to denoise the original sensor data as the data pretreatment step. The proposed method provides more accurate predictions than recursive least squares (RLSs) and a single RNN. In order to reduce the influence of sensor parameter errors and prediction model errors, the adaptive threshold is mentioned as a method for analyzing prediction errors. In the meantime, the results of the threshold analysis were combined with the qualitative force analysis to determine the rudder system’s fault diagnosis and location. Experiments conducted at sea demonstrate the feasibility and effectiveness of the proposed method.

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Section: Algorithm 1 Analysis Process For Normal Distributionmentioning

confidence: 99%

Data-Driven Fault Detection of AUV Rudder System: A Mixture Model Approach

Zhang

Zhang²,

Yan

et al. 2023

Machines

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“…Rao-Blackwellized implementations of PF, in which the linear and non-linear parts of the state vector are separated, have been extensively utilized in recent studies related to Terrain-Aided Navigation for underwater vehicles [19], [20], [21]. Rupeng et al proposes a terrain aided positioning (TAP) confidence interval model for PF to mitigate the initial TAN positioning error [22]. In [23], estimation performances of PF and Unscented Kalman Filter (UKF) are compared.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Hypothesis Filter for Terrain Aided Navigation

Tokta

Hocaoglu

2023

IEEE Access

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The susceptibility of GNSS signals has led to the development of advanced aiding systems in which information obtained from external sensors is fused with the inertial navigation solution to maintain navigation continuity in GNSS-denied environments. In terrain-aided navigation, the aim is to solve the horizontal position ambiguity of the platform with the help of pre-installed digital terrain elevation maps and estimated terrain height information obtained by the platform's barometer and distance measuring sensor readings. In this paper, we propose a novel multi-hypothesis terrain-aided navigation framework where horizontal position measurements are generated along with their covariance matrices from the estimated terrain heights. These measurements are fused with appropriate navigation hypotheses in a closed-loop configuration where augmented navigation error state estimates are fed into the navigation solution of the corresponding hypothesis. The number of hypotheses and their covariances change adaptively based on the terrain under the flight path preventing deviation from the true position for multi-modal or non-informative regions. The simulation results show that, unlike conventional TAN methods, the proposed method is able to yield accurate positioning estimations even when low-end IMUs are used. Furthermore, the proposed method is found to be effective in the presence of substantial initial errors in position, velocity, and heading.INDEX TERMS Inertial navigation, terrain referenced positioning, terrain aided navigation.

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“…Examples of PF in underwater TBN can be found in the literature [17,18]. Considering the sensitivity of PF in the initialization stage, a confidence interval model has been proposed to improve the convergence of PF [19]. To determine the number of particles adaptively in an efficient manner, an improved Kullback-Leibler distance PF was proposed in [20].…”

Section: Introductionmentioning

confidence: 99%

Particle filter underwater terrain-aided navigation based on gradient fitting

Zhou¹,

Wang²,

Gao³

et al. 2022

Meas. Sci. Technol.

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The Inertial Navigation System (INS) is widely used for Autonomous Underwater Vehicles (AUVs) navigation with excellent short-term precision. However, the positioning error of INS accumulates along with time. It is imperative to appeal to other approaches to mitigate drift errors, especially for long-term sailing tasks. To that end, underwater Terrain Based Navigation (TBN) is effective in bounding the drift errors. In particular, the particle filtering (PF) method is extensively employed in TBN to tackle the highly nonlinear measurement equation. In our previous work, the statistical properties of the Digital Terrain Model (DTM) gradient were exploited, and we proposed to use three probability density functions (PDFs) to characterize the normalized data, i.e., Gaussian, Gamma and Weibull distributions. In this paper, the likelihood was modified according to the gradient fitting results, i.e., an optimal distribution selection. Moreover, to prevent the measurement data with large gradients from generating particles with lower weights, a pre-screen procedure was proposed to stabilize PF sampling. As shown and demonstrated in simulations, our proposed improved PF method outperforms the other comparative ones in terms of Root Mean Square Error (RMSE) and stability, as well as accuracy, especially for flat terrain data. In terms of computation cost, employing smaller amount of measurement data, the proposed method is faster than the standard PF method.

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Improvements to Terrain Aided Navigation Accuracy in Deep-Sea Space by High Precision Particle Filter initialization

Cited by 12 publications

References 19 publications

Data-Driven Fault Detection of AUV Rudder System: A Mixture Model Approach

Data-Driven Fault Detection of AUV Rudder System: A Mixture Model Approach

A Novel Multi-Hypothesis Filter for Terrain Aided Navigation

Particle filter underwater terrain-aided navigation based on gradient fitting

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