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

Sung, Chang-Ky; Lee, Sang Jeong

doi:10.1007/s12555-018-0660-2

Cited by 6 publications

(7 citation statements)

References 17 publications

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“…If the interval between grids is greater than about 1.5 times the standard deviation of the process noise, the kernel for probability diffusion by sampling does not adequately reflect the statistical characteristics of the original noise [ 23 ]. VAGK, MMGK, and DWC have been proposed to resolve this problem [ 23 , 24 , 25 ]. VAGK generation method creates the kernel according to the conventional method and scales up it to match only the variance.…”

Section: Pmf With Reliable Time Propagationmentioning

confidence: 99%

“…The MMGK generation method is as follows [ 24 ]. First, let

be the k -th moment of the process noise whose mean and variance are

and

, respectively.…”

Section: Pmf With Reliable Time Propagationmentioning

confidence: 99%

“…This causes a problem in which the probability diffusion is not properly handled so the filter performance might deteriorate [ 23 ]. To resolve this problem, Variance Adjusted Gaussian Kernel (VAGK) or Moment Matched Gaussain Kernel (MMGK) have been proposed [ 23 , 24 ]. As another countermeasure, the Density-Weighted Convolution (DWC) algorithm, dealing with a model that only the measurement model is nonlinear, has been proposed recently [ 25 ].…”

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%

“…The MMGK generation method is as follows [ 24 ]. First, let

be the k -th moment of the process noise whose mean and variance are

and

, respectively.…”

Section: Pmf With Reliable Time Propagationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reliable Time Propagation Algorithms for PMF and RBPMF

Sung

Lee

2021

Sensors

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“…INS (inertial navigation system) is a device that calculates navigation information such as position, velocity, and attitude using measurements of the IMU (inertial measurement unit) [ 1 , 2 , 3 , 4 ]. INS is widely used in various fields such as weapons systems, robots, and aerospace because it can calculate accurate navigation information without the help of external sensors.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Gyro Bias Depending on the Position of Inertial Measurement Unit in Rotational Inertial Navigation Systems

Seo

Ryu

et al. 2022

Sensors

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In this paper, a calibration method for gyro bias that changes depending on the position of the IMU (inertial measurement unit) is proposed to improve the navigation performance of RLG-based RINS (ring-laser-gyro-based rotational inertial navigation system). RINS is a navigation device that compensates for the inertial sensor errors by utilizing the rotation of the IMU. In previous studies, the rotation scheme of the IMU is designed assuming that inertial sensor errors are not affected by position of the IMU. However, changes in temperature distribution, direction of gravity, and dithering according to the rotation of the IMU affect the inertial sensor errors, such as gyro bias. These errors could degrade the long-term navigation performance of RLG-based RINS. To deal with this problem, this paper proposed a compensation method of the gyro bias that changes depending on the position of the IMU. First, RINS is reviewed using a dual-axis 16-position rotation scheme and RLG. Next, the attitude error of RLG-based RINS is derived utilizing navigation equations. The effect of the gyro bias change caused by the change in the IMU attitude for the navigation performance of RINS is analyzed based on navigation equations and simulations. Finally, system-level indirect calibrations for the Z–axis up position and Z–axis down position are performed to calculate the gyro bias change caused by the IMU attitude. The accuracy of the proposed calibration method is verified by long-term navigation test. The test results show that the proposed calibration method improves the navigation performance of RINS compared with the conventional calibration method.

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“…This is a crucial disadvantage, particularly in military application. To overcome this issue, many alternative navigation technologies have been studied [1–3]. One of the major alternative methods is terrain referenced navigation (TAN), which uses terrain information to perform independent navigation and compare the correlation of the vehicle's altitude and digital terrain elevation data.…”

Section: Introductionmentioning

confidence: 99%