Multirate sensor fusion for GPS using Kalman filtering, fuzzy methods, and map matching

Mayhew, David; Kachroo, Pushkin

doi:10.1117/12.335722

Cited by 13 publications

(13 citation statements)

References 4 publications

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“…There are conversion rates which convert degrees to meters in [12]. The conversion formula is where ϕ is the geodetic latitude.…”

Section: Kalman Filter Modulementioning

confidence: 99%

Sensor-Aided Personal Navigation Systems for Handheld Devices

Chou

et al. 2010

2010 39th International Conference on Parallel Processing Workshops

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The positioning technique is the key technique for developing geographic applications, like location based services. The Global Positioning System (GPS) is a common approach for positioning in vehicular navigations. Although GPS can provide absolute position information, the accuracy of GPS is not enough for personal navigations. What is worse, GPS does not work well indoors. Instead, Inertial Measurement Units (IMUs) can be used to track objects with high precision, but it provides relative position information. Thus, integration of GPS and IMU can do positioning indoors and outdoors. In this paper, combining our previous work, a pedestrian tracking system for handheld devices, with GPS leads to a personal navigation system for handheld devices. The position and heading information can be calculated from this system. The system also serves a platform for many applications related to the location.

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“…There are conversion rates which convert degrees to meters in [12]. The conversion formula is where ϕ is the geodetic latitude.…”

Section: Kalman Filter Modulementioning

confidence: 99%

Sensor-Aided Personal Navigation Systems for Handheld Devices

Chou

et al. 2010

2010 39th International Conference on Parallel Processing Workshops

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“…In this paper, Kalman filter is implemented to reduce GPS errors and thus increase the accuracy of the localization system [19][20][21]. Our goal is to provide the same precision as Differential GPS (DGPS) systems.…”

Section: Error Correction Of In-vehicle Gps Coordinates Using Kalman mentioning

confidence: 99%

Hybrid GPS-GSM Localization of Automobile Tracking System

Alkhedher¹

2011

IJCSIT

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“…Where the INS outputs are compared to the outputs of the GPS. The errors in between are subjected to Kalman filtering, which enhances the performance of the navigation system by removing the effect of residual random errors during the surveying process (Mayhew, 1999).…”

Section: Overviewmentioning

confidence: 99%

Ga Based Data Fusion Approach in an Intelligent Integrated GPS/Ins System

Asadian

Moshiri

Sedigh

et al. 2005

Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics

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A new concept regarding to the GPS/INS integration, based on artificial intelligence here is presented. Most integrated inertial navigation systems (INS) and global positioning systems (GPS) have been implemented using the Kalman filtering technique with its drawbacks related to the need for predefined INS error model and observability of at least four satellites. Most recently, an INS/GPS integration method using a hybridadaptive network based fuzzy inference system (ANFIS) has been proposed in literature. During the availability of GPS signal, the ANFIS is trained to map the error between the GPS and the INS. Then it will be used to predict the error of the INS position components during GPS signal blockage. As ANFIS will be employed in real time applications, the change in the system parameters (e.g., the number of membership functions, the step size, and step increase and decrease rates) to achieve the minimum training error during each time period is automated. This paper introduces a genetic optimization algorithm that is used to update the ANFIS parameters with the INS/GPS error function used as the objective function to be minimized. The results demonstrate the advantages of the genetically optimized ANFIS for INS/GPS Integration in comparison with conventional ANFIS specially in the cases when facing satellites' outages. Coping with this problem plays an important role in assessment of the fusion approach in land navigation.

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Multirate sensor fusion for GPS using Kalman filtering, fuzzy methods, and map matching

Cited by 13 publications

References 4 publications

Sensor-Aided Personal Navigation Systems for Handheld Devices

Sensor-Aided Personal Navigation Systems for Handheld Devices

Hybrid GPS-GSM Localization of Automobile Tracking System

Ga Based Data Fusion Approach in an Intelligent Integrated GPS/Ins System

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