Grid Support Adaptation for Point Mass Filter Based Terrain Referenced Navigation Using Mutual Information

Park, Yong-gonjong; Park, Chan Gook

doi:10.1109/jsen.2018.2862941

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…AGD assumes Gaussian distribution when selecting grid support, whereas BGD is a grid support selection algorithm that considers non-Gaussian distribution. Suitable algorithms for TRN include the grid resolution/support adaption algorithm considering noise magnitude [ 19 ], and the grid support adaption algorithm using mutual information [ 20 ]. The density specific grid design algorithm that assumes two different grids [ 21 ] and the density difference grid design algorithm based on the differentiation of the PDF in a sparse grid [ 22 ] have been presented recently for general estimation problems.…”

Section: Pmf With Reliable Time Propagationmentioning

confidence: 99%

“…Most of the studies have been conducted for the purpose of improving TRN performance, but the results are applicable to general estimation problems. Numerous research results for efficient grid design or reselection have been presented to improve PMF performance, and there are the Anticipative Grid Design (AGD) algorithm and the Boundary-based Grid Design (BGD) algorithm [ 18 ], the grid resolution and support design algorithm considering a noise level in TRN [ 19 ], the grid support design algorithm using mutual information [ 20 ], the density specific grid design algorithm assuming two different grids [ 21 ], and the density difference grid design algorithm based on the differentiation of the PDF in a sparse grid [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Reliable Time Propagation Algorithms for PMF and RBPMF

Sung

Lee

2021

Sensors

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This paper addresses the reliable time propagation algorithms for Point Mass Filter (PMF) and Rao–Blackwellized PMF (RBPMF) for the nonlinear estimaton problem. The conventional PMF and RBPMF process the probability diffusion for the time propagation with the direct sampled-values of the process noise. However, if the grid interval is not dense enough, it fails to represent the statistical characteristics of the noise accurately so the performance might deteriorate. To overcome that problem, we propose time propagation convolution algorithms adopting Moment Matched Gaussian Kernel (MMGK) on regular grids through mass linear interpolation. To extend the dimension of the MMGK that can accurately describe the noise moments up to the kernel length, we propose the extended MMGK based on the outer tensor product. The proposed time propagation algorithms using one common kernel through the mass linear interpolation not only improve the performance of the filter but also significantly reduce the computational load. The performance improvement and the computational load reduction of the proposed algorithms are verified through numerical simulations for various nonlinear models.

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Section: Pmf With Reliable Time Propagationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reliable Time Propagation Algorithms for PMF and RBPMF

Sung

Lee

2021

Sensors

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“…The purpose of the initial phase is to reduce the positioning deviation of the starting point, to ensure that the TAN system can converge steadily and track to the correct position in the tracking filtering stage as shown in Fig.3. In the past 20 years, underwater TAN research has been focused on tracking filtering methods, such as PF (particle filter) [8]- [10], robust PF [11]- [13], and PMF (point mass filter) [14]- [18]. The PF method is the best filter method for TAN systems because of its advantages in dealing with the state estimation of non-linear and non-Gaussian systems [19].…”

Section: Related Workmentioning

confidence: 99%

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

Wang

et al. 2020

IEEE Access

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AUV (autonomous underwater vehicles) are required to have long-term and high-precision positioning capability relative to seabed targets in most deep-sea exploration tasks. However, acoustic positioning error is positively correlated with its operating range and inertial navigation has inevitable accumulated time errors, neither of which provide precise AUV positions. TAN (terrain aided navigation) directly calculates the AUV position to the seabed terrain coordinate system by tracking the seabed topographic characteristics, which can guide the AUV to seabed target accurately. However, the initial TAN positioning error will increase with the AUV operation depth, which causes a large PF (particle filter) initialization error and particle coverage interval, and will affect the convergence and stability of TAN. To solve this problem, we first propose a TAP (terrain aided position) confidence interval model. We then use the confidence interval to constrain the initial particles to a smaller range. Finally, the validity of the algorithm is verified by playback simulation with ship-borne multi-beam sonar sensor measured data. The results show that the TAP confidence interval can reduce the coverage of the initial particle, and can improve the convergence speed and filtering accuracy of the TAN.

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Moment Matched Gaussian Kernel and Region Representative Likelihood for Performance Improvement of PMF-based TRN

Sung

Lee

2020

Int. J. Control Autom. Syst.

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Grid Support Adaptation for Point Mass Filter Based Terrain Referenced Navigation Using Mutual Information

Cited by 8 publications

References 23 publications

Reliable Time Propagation Algorithms for PMF and RBPMF

Reliable Time Propagation Algorithms for PMF and RBPMF

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

Moment Matched Gaussian Kernel and Region Representative Likelihood for Performance Improvement of PMF-based TRN

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