GPS Positon Predicting System by Kalman Filter with Velocity from OBD and Direction from Magnetometer

Chunhakam, Puripak; Pummarin, Phurit; Jeen-im, Purichaya; Wardkien, Paramote; Wisartpong, P.; Lertteerada, Krittapas

doi:10.1109/ieecon51072.2021.9440239

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…However, this solution necessitates employing relatively sophisticated onboard INS equipment and is confined to a singular testing route. Chunhakam et al [9] introduced a navigation algorithm employing magnetometers and speed data retrieved via truck on-board diagnostics (OBD), yielding an evaluated error of 30 m. The reliance on speed information obtained exclusively from the truck's OBD system limits their…”

Section: Discussionmentioning

confidence: 99%

“…Sensors 2023, 23, x FOR PEER REVIEW 6 of 14 (9) Upon container stoppage, store w . When the container resumes travel, retrieve w for subsequent calculations.…”

Section: Adjustment Of Container Positions Using Trajectory-based Map...mentioning

confidence: 99%

“…To address these limitations, various solutions have been proposed. As shown in Table 1, many researchers have sought to address the issue of positioning loss due to GPSdenied areas by employing inertial sensor data to predict trajectories [6][7][8][9]. This approach involves continuously tracking a vehicle's position based on acceleration, direction, and attitude data provided by inertial sensors, assuming an initial position is available.…”

Section: Introductionmentioning

confidence: 99%

“…If the GPS call frequency is too low, the container may fail to report those important events. A more innovative approach involves utilizing vehicle state data to predict vehicle location during GPS unavailability [9,16]. This approach primarily relies on state prediction through the application of Kalman filtering.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

ConGPS: A Smart Container Positioning System Using Inertial Sensor and Electronic Map with Infrequent GPS

Huang,

Song,

Choi

et al. 2023

Sensors

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Real-time global positioning is important for container-based logistics. However, a challenge in real-time global positioning arises from the frequency of both global positioning system (GPS) calls and GPS-denied environments during transportation. This paper proposes a novel system named ConGPS that integrates both inertial sensor and electronic map data. ConGPS estimates the speed and heading direction of a moving container based on the inertial sensor data, the container trajectory, and the speed limit information provided by an electronic map. The directional information from magnetometers, coupled with map-matching algorithms, is employed to compute container trajectories and current positions. ConGPS significantly reduces the frequency of GPS calls required to maintain an accurate current position. To evaluate the accuracy of the system, 280 min of driving data, covering a distance of 360 km, are collected. The results demonstrate that ConGPS can maintain positioning accuracy within a GPS-call interval of 15 min, even if using low-cost inertial sensors in GPS-denied environments.

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Section: Discussionmentioning

confidence: 99%

“…Sensors 2023, 23, x FOR PEER REVIEW 6 of 14 (9) Upon container stoppage, store w . When the container resumes travel, retrieve w for subsequent calculations.…”

Section: Adjustment Of Container Positions Using Trajectory-based Map...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ConGPS: A Smart Container Positioning System Using Inertial Sensor and Electronic Map with Infrequent GPS

Huang,

Song,

Choi

et al. 2023

Sensors

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show abstract

“…Therefore, we use the Kalman filter to get a smooth and stable RSSI value. The previous study has been clearly explained that the Kalman filter has a significant impact to smoothen the value of moving objects [24]- [25]. In this system, we proposed to use the Kalman filter, which we added from the android library.…”

Section: B Distance Measurement Analysis (Notification For Social Distancing Performance)mentioning

confidence: 99%

SIMONIC: IoT Based Quarantine Monitoring System for Covid-19

Mardiana

Martadinata

Nurkahfi

et al. 2021

J. Elektron. dan Telekomun.

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COVID-19, which has become a global pandemic since March 2020, has tremendously affected human life globally. The negative impact of COVID-19 affects societies in almost all aspects. Implementing quarantine monitoring, also social distancing, and contact tracing are a series of processes that can suppress the new infected COVID-19 cases in various countries. Prior works have proposed different monitoring systems to assist the monitoring of individuals in quarantines, as well as many methods are offered for social distancing and contact tracing. These methods focus on one function to provide a reliable system. In this paper, we propose IoT-based quarantine monitoring by implementing a geofence equipped with social distancing features to offer an integrated system that provides more benefits than one system carrying one particular function. We propose a system consisting of a low cost, low complexity, and reusable wristband design and mobile apps to support the quarantine monitoring system. For the geofencing, we propose a GPS-based geofence system that was developed by taking advantage of the convenience offered by the Traccar application. Meanwhile, we add the notification for social distancing feature with adaptive distance measurement RSSI-based set up in the android application. Based on the experiment we did to validate the system, in terms of wristband-to-smartphone communication, scanning interval in smartphone and advertising interval in wristband is best to set in 7 s for both. For social distancing notification and geofence, we measure the system performance through precision, recall, accuracy, and F-measure.

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Development of microcontroller-based sound level meter with Kalman filter for noise reduction

Salsabila,

Purwowibowo,

Sanjaya

et al. 2024

AIP Conference Proceedings

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GPS Positon Predicting System by Kalman Filter with Velocity from OBD and Direction from Magnetometer

Cited by 6 publications

References 4 publications

ConGPS: A Smart Container Positioning System Using Inertial Sensor and Electronic Map with Infrequent GPS

ConGPS: A Smart Container Positioning System Using Inertial Sensor and Electronic Map with Infrequent GPS

SIMONIC: IoT Based Quarantine Monitoring System for Covid-19

Development of microcontroller-based sound level meter with Kalman filter for noise reduction

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