Marginalized Point Mass Filter with Estimating Tidal Depth Bias for Underwater Terrain-Aided Navigation

Peng, Dongdong; Zhou, Tian; Xu, Chao; Zhang, Wanyuan; Shen, Jiajun

doi:10.1155/2019/7340130

Cited by 15 publications

(5 citation statements)

References 22 publications

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“…For this problem, Peng proposed the interpolation technique of the height errors according to changing grid intervals. However, his method is an index-based adaptive method that simply copies neighbor height error intuitively [ 30 ], which is not theoretical and also has a disadvantage that it cannot be applied to changes in grid position. On the other hand, the proposed redefinition algorithm of the linear part is more systematic and can be applied to both the grid position and the interval change.…”

Section: Rao–blackwellized Pmf With Reliable Time Propagationmentioning

confidence: 99%

“…To complete this scheme, a linear part redefinition procedure following the nonlinear part mass redefinition is required. However, until now, only the index-based adaption algorithm of simply copying the state of the neighboring linear part for the TRN problem has been proposed [ 30 ]. Therefore, for the constant linear model case, we propose the RBPMF algorithm with indirect time propagation, which includes the redefinition process of the linear part, as the third result of this paper.…”

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: Rao–blackwellized 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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“…Since acquiring information from a map is a highly non-linear operation, a particle filter method is applied to TAN for state estimation. Subsequent work has improved the TAN method by plugging in different kinds of range sensors [5], [6], upgrading the particle filter into different variants [7]- [9], developing efficient mapping features [10]- [12], and realizing cooperative TAN with multiple vehicles [13]- [15]. However, the success of a TAN system requires sufficient excitation from terrain elevation, which is not always guaranteed in many scenarios.…”

Section: Related Workmentioning

confidence: 99%

Learning Cross-Scale Visual Representations for Real-Time Image Geo-Localization

Zhang,

Johnson-Roberson

2021

Preprint

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Robot localization remains a challenging task in GPS denied environments. State estimation approaches based on local sensors, e.g. cameras or IMUs, are drifting-prone for long-range missions as error accumulates. In this study, we aim to address this problem by localizing image observations in a 2D multi-modal geospatial map. We introduce the crossscale i dataset and a methodology to produce additional data from cross-modality sources. We propose a framework that learns cross-scale visual representations without supervision. Experiments are conducted on data from two different domains, underwater and aerial. In contrast to existing studies in crossview image geo-localization, our approach a) performs better on smaller-scale multi-modal maps; b) is more computationally efficient for real-time applications; c) can serve directly in concert with state estimation pipelines. Our code and data are released at https://github.com/tyz1030/CroScaleRep.git I. INTRODUCTIONGeo-localization plays a key role in autonomous and robotic systems exploring a priori unknown environments in the wild. To achieve better localization accuracy, a wide range of sensors have been used on today's field robots. According to the reference frame, sensors can be generally categorized as local or global. Local sensors, e.g. cameras and inertial measurement units (IMUs), observe the environment in a local coordinate frame. Global sensors, e.g. global positioning systems (GPS), barometers, and magnetometers, provide global measurements in fixed global frames. While local sensors give high-precision local measurements, global sensors are noisier but do not suffer from the same drift effects when localizing the vehicle. The algorithmic combination of both kinds of sensors achieves locally accurate and globally drift-free performance on long-range tasks [1].However, there are many scenarios where global information is not available or only partially available. The scenarios can be underwater, underground, or other GPS denied environments. Taking underwater as an example, neither GPS nor land-based station towers can be accessed since electromagnetic waves are heavily attenuated. Acoustic localization gets downgraded by variation in salinity or temperature in the water body. Magnetometer and depth sensors are reliable global sensors underwater, however, they only provide measurements up to 4 degrees of freedom (DOFs)

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“…In recent years, with the constant exploration of oceans, multibeam sonar (MBS) is being applied in many scenarios, such as underwater environment mapping [1], underwater target detection [2], and underwater terrain-aided navigation [3]. However, noise interference is strong in water, which makes it more difficult to extract useful information from the images.…”

Section: Introductionmentioning

confidence: 99%

A SIFT-Like Feature Detector and Descriptor for Multibeam Sonar Imaging

Zhang

Zhou

et al. 2021

Journal of Sensors

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Multibeam imaging sonar has become an increasingly important tool in the field of underwater object detection and description. In recent years, the scale-invariant feature transform (SIFT) algorithm has been widely adopted to obtain stable features of objects in sonar images but does not perform well on multibeam sonar images due to its sensitivity to speckle noise. In this paper, we introduce MBS-SIFT, a SIFT-like feature detector and descriptor for multibeam sonar images. This algorithm contains a feature detector followed by a local feature descriptor. A new gradient definition robust to speckle noise is presented to detect extrema in scale space, and then, interest points are filtered and located. It is also used to assign orientation and generate descriptors of interest points. Simulations and experiments demonstrate that the proposed method can capture features of underwater objects more accurately than existing approaches.

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Marginalized Point Mass Filter with Estimating Tidal Depth Bias for Underwater Terrain-Aided Navigation

Cited by 15 publications

References 22 publications

Reliable Time Propagation Algorithms for PMF and RBPMF

Reliable Time Propagation Algorithms for PMF and RBPMF

Learning Cross-Scale Visual Representations for Real-Time Image Geo-Localization

A SIFT-Like Feature Detector and Descriptor for Multibeam Sonar Imaging

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