Terrain Matching Localization for Underwater Vehicle Based on Gradient Fitting

Gao, Junhui; Peng, Dongdong; Zhou, Tian; Wang, Tianhao; Xu, Chao

doi:10.1155/2018/3717430

Cited by 12 publications

(5 citation statements)

References 21 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The bathymetric beams are independent of each other, and thus all the distributions are defined based on the bathymetric beam difference (equation ( 19)). The parameters a, b, λ and K can be estimated according to [24] as follows:…”

Section: Improved Pfmentioning

confidence: 99%

“…(3) Gradient fitting: choose a proper likelihood from equations ( 16)-( 18) according to historical data; (4) Pre-screen and correction: according to equation (24), for the filtered data ∆y k and the likelihood p(y k |x k ), calculate the weights…”

Section: Improved Pfmentioning

confidence: 99%

“…Particle filter based on gradient fitting (PFG, our proposed method): the measurement data are filtered according to equation (24), and the type of likelihood is chosen and updated every 10 s in the simulation.…”

Section: Simulationsmentioning

confidence: 99%

“…Currently, most of the improved PF methods devote themselves to building and exploring more sophisticated models. In [24], the statistical properties of the DTM gradient were exploited and three probability density functions (PDFs) invoked to characterize the normalized data. Based on the conclusion in [24], an improved PF algorithm is proposed in this paper using the PDFs and the gradient fitting results.…”

Section: Introductionmentioning

confidence: 99%

“…In [24], the statistical properties of the DTM gradient were exploited and three probability density functions (PDFs) invoked to characterize the normalized data. Based on the conclusion in [24], an improved PF algorithm is proposed in this paper using the PDFs and the gradient fitting results. The technical contribution of this work can be summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Particle filter underwater terrain-aided navigation based on gradient fitting

Zhou¹,

Wang²,

Gao³

et al. 2022

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Improved Pfmentioning

confidence: 99%

Section: Improved Pfmentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations