Kalman Filter and Its Application

Li, Qiang; Li, Ranyang; Ji, Kaifan; Dai, Wei

doi:10.1109/icinis.2015.35

Cited by 287 publications

(159 citation statements)

References 18 publications

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“…The former provides a measure of the calibration precision for each single camera; the latter is a proxy for the precision of the entire stereo rig. For our calibration, we obtained e R = 0.5 px and e E = 0.05 px, which are low and within the acceptable range used in other stereo systems (Smith ; Balletti et al ; Shortis ; Li et al , b ; Rathnayaka et al ).…”

Section: Materials and Proceduressupporting

confidence: 68%

“…, dark gray dots) within a searching area (blue rectangle) with width d MAX − d MIN and height 15 px. The height was set to take into account deviations of

y_{i 2 k}^{C}

with respect to

y_{i 1 k}^{C}

due to the calibration parameters, additional small distortions introduced by flowing water (Li et al , b ) and the fact that the videos were out of sync by τ frames. The matrix representations M i 1 k of particle P i 1 k and the matrixes M i 2 m of particles

{\overset{false^}{P}}_{i 2 k}

were then extracted from the frames based on the coordinates of their bounding boxes (blue dots in Fig.…”

Section: Materials and Proceduresmentioning

confidence: 99%

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A low‐cost underwater particle tracking velocimetry system for measuring in situ particle flux and sedimentation rate in low‐turbulence environments

Simoncelli

Kirillin

Толомеев

et al. 2019

Limnology & Ocean Methods

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We describe a low‐cost three‐dimensional underwater particle tracking velocimetry system to directly measure particle settling rate and flux in low‐turbulence aquatic environments. The system consists of two waterproof cameras that acquire stereoscopic videos of sinking particles at 48 frames s−1 over a tunable sampling volume of about 45 × 25 × 24 cm. A dedicated software package has been developed to allow evaluation of particle velocities, concentration and flux, but also of morphometric parameters such as particle area, sinking angle, shape irregularity, and density. Our method offers several advantages over traditional approaches, like sediment trap or expensive in situ camera systems: (1) it does not require beforehand particle collection and handling; (2) it is not subjected to sediment trap biases from turbulence, horizontal advection, or presence of swimmers, that may alter particulate load and flux; (3) the camera system enables faster data processing and flux computation at higher spatial resolution; (4) apart from the particle settling rates, the particle size distribution, and morphology is determined. We tested the camera system in Lake Stechlin (Germany) in low turbulence and mean flow, and analyzed the morphological properties and settling rates of particles to determine their sinking behavior. The particle flux assessed from conventional sediment trap measurements agreed well with that determined by our system. By this, the low‐cost approach demonstrated its reliability in low turbulence environments and a strong potential to provide new insights into particulate carbon transport in aquatic systems. Extension of the method to more turbulent and advective conditions is also discussed.

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Section: Materials and Proceduressupporting

confidence: 68%

“…, dark gray dots) within a searching area (blue rectangle) with width d MAX − d MIN and height 15 px. The height was set to take into account deviations of

y_{i 2 k}^{C}

with respect to

y_{i 1 k}^{C}

{\overset{false^}{P}}_{i 2 k}

were then extracted from the frames based on the coordinates of their bounding boxes (blue dots in Fig.…”

Section: Materials and Proceduresmentioning

confidence: 99%

A low‐cost underwater particle tracking velocimetry system for measuring in situ particle flux and sedimentation rate in low‐turbulence environments

Simoncelli

Kirillin

Толомеев

et al. 2019

Limnology & Ocean Methods

View full text Add to dashboard Cite

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“…The proposed methods are the Big Bang-Big Crunch (BB-BC) optimization algorithm [30,31] and the Genetic Algorithm [32][33][34] for eliminating the offset value (bias cancellation) and optimization, the Kalman Filter [35][36][37] for removing outliers and noise caused by interference:…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…The Kalman Filter (KF) algorithm uses a serial data, which may contain noise, and is observed over time. The main goal is to increase the accuracy in estimation of the unknown variables [35]. The KF was proposed firstly by Rudolf Emil Kálmán in 1960 [38], and it became a standard approach to achieve optimal estimation.…”

Section: The Kalman Filter (Kf)mentioning

confidence: 99%

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Accurate indoor positioning with ultra-wide band sensors

Arsan¹

2020

Turk J Elec Eng & Comp Sci

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Ultra-wide band is one of the emerging indoor positioning technologies. In the application phase, accuracy and interference are important criteria of indoor positioning systems. Not only the method used in positioning, but also the algorithms used in improving the accuracy is a key factor. In this paper, we tried to eliminate the effects of off-set and noise in the data of the ultra-wide band sensor-based indoor positioning system. For this purpose, optimization algorithms and filters have been applied to the raw data, and the accuracy has been improved. A test bed with the dimensions of 7.35 m × 5.41 m and 50 cm × 50 cm grids has been selected, and a total of 27,000 measurements have been collected from 180 test points. The average positioning error of this test bed is calculated as 16.34 cm . Then, several combinations of algorithms are applied to raw data. The combination of Big Bang-Big Crunch algorithm for optimization, and then the Kalman Filter have yielded the most accurate results. Briefly, the average positioning error has been reduced from 16.34 cm to 7.43 cm . Key words: Indoor positioning, ultra-wide band sensors, optimization, big bang-big crunch algorithm, genetic algorithms, the Kalman filter (A-GPS) technology is a navigation system used on mobile devices that helps to identify a user's location via the base station's A-GPS address server [3]. The accuracy provided by the GPS in determining the outdoor location is between 3 m and 15 m , usually around 10 m . The accuracy that A-GPS provides in determining the outdoor location is about 15 m, while the interior is 50 m [3]. As can be seen from the accuracy values, these solutions are sufficient to determine the outdoor position, in other words, the position in the open space.On the other hand, given the fact that people now spend more than 80% of their time indoors, a suitable system for locating interior space is needed. Unfortunately, the use of GPS satellites in indoor location determination is not possible as GPS signals are weaken due to atmospheric delays, multi-paths, steel structures, roofs and building walls [1][2][3]. For this reason, efforts have been made to develop new technologies that allow reliable indoor location determination with high accuracy, low average positioning error over the past two decades.

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