Applying fuzzy logic to the Kalman filter divergence problem

Abdelnour, G.; Chand, S.; Chiu, S.

doi:10.1109/icsmc.1993.384814

Cited by 27 publications

(22 citation statements)

References 2 publications

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“…For an optimum Kalman filter, an innovation vector of zero mean Gaussian white noise (Abdelnour et al, 1993). Therefore, the performance of the Kalman filter can be monitored using the value of the innovation vector.…”

Section: Divergence Detection and Fuzzy Logic Correctionmentioning

confidence: 99%

“…Therefore, if a white-noise model is assumed for the process and measurements, the reliability factor of a sensor in the Kalman filter has to be constantly updated. Abdelnour et al (1993) used fuzzy logic in detecting and correcting the divergence. Sasladek and Wang (1999) used fuzzy logic with an extended Kalman filter to tackle the problem of divergence for an autonomous ground vehicle.…”

Section: Approachmentioning

confidence: 99%

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Sensor Fusion Using Fuzzy Logic Enhanced Kalman Filter for Autonomous Vehicle Guidance in Citrus Groves

Subramanian¹,

Burks²,

Dixon³

2009

Transactions of the ASABE

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ABSTRACT. This article discusses the development of a sensor fusion system for guiding an autonomous vehicle through citrus grove alleyways. The sensor system for path finding consists of machine vision and laser radar. An inertial measurement unit (IMU) is used for detecting the tilt of the vehicle, and a speed sensor is used to find the travel speed. A fuzzy logic enhancedhere is a current need in the Florida citrus industry to automate citrus grove operations. This need is due to reduction in the availability of labor, rising labor cost, and potential immigration challenges. Autonomous vehicles would be an important part of an automated citrus grove. Autonomous vehicles can operate accurately for a longer duration of operation than when using a human driver. In a scenario where an automated citrus harvester is used, the operator can monitor both the harvester and the vehicle instead of being solely the driver. Based on citrus groves identified for testing the autonomous navigation of the tractor used by Subramanian et al. (2006), it was estimated that the ability to navigate in the middle of the identified citrus alleyways with an accuracy of 15 cm deviation from the center would be acceptable. In general, this estimate is dependent on the size of the vehicle, the alleyway width, layout of the citrus groves, and the application for which the autonomous vehicle is used. Numerous autonomous vehicles for agricultural applications have been described in the literature. The guidance systems developed by Noguchi et al. (2002) and Chen et al. (2003) were for operation in paddy fields for transplanting rice, whereas the guidance system de- Machine vision is a useful sensing methodology for guidance. Its long range aids in faster decision making before a hard turn is to be performed or for faster obstacle detection and is quite robust to minor variations in the path boundaries. However, it lacks the centimeter-level accuracy of laser radar in detecting the distance to close range path boundaries or obstacles. The accuracy of ladar is useful in keeping the vehicle accurately positioned in the path. However, this accuracy makes the information noisy if there are minor variations in the path boundaries. The development of an autonomous vehicle guidance system for citrus grove navigation using machine vision and laser radar (ladar) based guidance systems was discussed by Subramanian et al. (2006). Both machine vision and ladar based guidance performed with a maximum error of 6.1 cm in test paths. The machine vision based guidance system had also guided a tractor through an alleyway of a citrus grove, keeping the vehicle visually in the center of the path with no collisions with the trees. It was found that ladar based guidance was more accurate than vision based guidance in well defined paths within a speed of 3.1 m s -1 , whereas the vision based guidance was able to keep the vehicle in the middle of the path in several types of paths with less accuracy. In the approach taken in previous research, the uneven tree canopy...

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Section: Divergence Detection and Fuzzy Logic Correctionmentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

Sensor Fusion Using Fuzzy Logic Enhanced Kalman Filter for Autonomous Vehicle Guidance in Citrus Groves

Subramanian¹,

Burks²,

Dixon³

2009

Transactions of the ASABE

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“…For an optimal Kalman filtering, the innovation vector should be a zero mean white noise (Abdelnour et al, 1993).…”

Section: Imumentioning

confidence: 99%

Development of Positioning System Based on Auto VRS-GPS Surveying

Choi¹,

Kim²,

Park³

2014

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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There has been a need for replacing human labors with a robot in such dangerous and hard jobs of various construction sites. For that reason, many researches have been made about the high quality robot, which performs its duty instead of human labors. This study is about auto surveying system development based on VRS-GPS which enables autodriving in dangerous areas where it's difficult for humans to measure directly. This study is about the auto-surveying system development, based on VRS-GPS, which enables auto-drive in dangerous areas, whereas difficult for humans to measure directly. The GPS is made with GRXI and SHC250 controllers of the SOKKIA company. The auto surveying system is composed of DPS module, geomagnetism sensor, bluetooth, gimbals, IMU, etc to automatic drive via enter into a route of position. The developed auto surveying system has installed the carmeras for front and vertical axis as well as systems to grasp situation of surveying with smartphone in real time. The result from analysed RMSE of auto surveying system and VRS-GPS surveying is 0.0169m of X-axis and 0.0246m of Y-axis. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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“…The process model and measurement model for the Kalman filter are represented as (9a) (9b) where the vectors and are both white noise sequences with zero means and mutually independent (10) where is the Dirac delta function, represents expectation, and superscript "T" denotes matrix transpose.…”

Section: B Gps Navigation Processing Using the Extended Kalman Filtementioning

confidence: 99%

“…Sasiadek et al introduced the Fuzzy Logic Adaptive System (FLAS) for adapting the process and measurement noise covariance matrices in navigation data fusion design [9]. Abdelnour et al used the exponential-weighting algorithm for detecting and correcting the divergence of the Kalman filter [10]. Kobayashi et al proposed a method for generating an accurate estimate of the absolute speed of a vehicle from noisy acceleration and erroneous wheel speed information [11].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Strong Tracking Extended Kalman Filtering for GPS Navigation

2007

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Abstract-The well-known extended Kalman filter (EKF) has been widely applied to the Global Positioning System (GPS) navigation processing. The adaptive algorithm has been one of the approaches to prevent the divergence problem of the EKF when precise knowledge on the system models are not available. One of the adaptive methods is called the strong tracking Kalman filter (STKF), which is essentially a nonlinear smoother algorithm that employs suboptimal multiple fading factors, in which the softening factors are involved. Traditional approach for selecting the softening factors heavily relies on personal experience or computer simulation. In order to resolve this shortcoming, a novel scheme called the adaptive fuzzy strong tracking Kalman filter (AFSTKF) is carried out. In the AFSTKF, the fuzzy logic reasoning system based on the Takagi-Sugeno (T-S) model is incorporated into the STKF. By monitoring the degree of divergence (DOD) parameters based on the innovation information, the fuzzy logic adaptive system (FLAS) is designed for dynamically adjusting the softening factor according to the change in vehicle dynamics. GPS navigation processing using the AFSTKF will be simulated to validate the effectiveness of the proposed strategy. The performance of the proposed scheme will be assessed and compared with those of conventional EKF and STKF.Index Terms-Adaptive extended Kalman filtering, fuzzy logic adaptive system (FLAS), global positioning system (GPS), strong tracking Kalman filter (STKF).

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Applying fuzzy logic to the Kalman filter divergence problem

Cited by 27 publications

References 2 publications

Sensor Fusion Using Fuzzy Logic Enhanced Kalman Filter for Autonomous Vehicle Guidance in Citrus Groves

Sensor Fusion Using Fuzzy Logic Enhanced Kalman Filter for Autonomous Vehicle Guidance in Citrus Groves

Development of Positioning System Based on Auto VRS-GPS Surveying

Adaptive Fuzzy Strong Tracking Extended Kalman Filtering for GPS Navigation

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