• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.10
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• pp.18-22
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• 2012

This paper suggests an altitude determination algorithm using GPS and barometric altitude sensors and evaluates the algorithm by digital map contour. A code based GPS altitude has lots of errors so that the car navigation companies can not use this data. Therefore, altitude is calculated by convergence data with GPS and barometric altitude variance in this paper. The modified altitudes are compared with the digital map contour and then this algorithm's effect is evaluated for the car navigation systems.

• Journal of Advanced Navigation Technology
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• v.15 no.6
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• pp.986-993
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• 2011

In this paper we consider the problem of the existing TCAS-II systems that fail to be satisfactory solution to mid-air collisions (MACs) and near mid-air collisions (NMACs or near misses). This is attributed to the fact that the earlier studies on the collision avoidance mainly have focused on determination logic of avoidance direction and vertical speed, reversal of the avoidance direction, multiple aircraft geometry, and availability in certain air spaces. But, the influence of sensor measurement errors on the performance of collision avoidance was not properly taken into account. Here we propose a new TCAS algorithm by using Kalman filter instead of '${\alpha}-{\beta}$' tracker to improve the avoidance performance under the influence of barometric sensor errors due to air-temperature, pressure leaks, static source error correction, etc.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.101-108
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• 2024

Indoor localization is a critical component for numerous applications, ranging from navigation in large buildings to emergency response. This paper presents an enhanced Pedestrian Dead Reckoning (PDR) scheme using smartphone sensors, integrating neural network-aided motion recognition, Kalman filter-based error correction, and multi-sensor data fusion. The proposed system leverages data from the accelerometer, magnetometer, gyroscope, and barometer to accurately estimate a user's position and orientation. A neural network processes sensor data to classify motion modes and provide real-time adjustments to stride length and heading calculations. The Kalman filter further refines these estimates, reducing cumulative errors and drift. Experimental results, collected using a smartphone across various floors of University, demonstrate the scheme's ability to accurately track vertical movements and changes in heading direction. Comparative analyses show that the proposed CNN-LSTM model outperforms conventional CNN and Deep CNN models in angle prediction. Additionally, the integration of barometric pressure data enables precise floor level detection, enhancing the system's robustness in multi-story environments. Proposed comprehensive approach significantly improves the accuracy and reliability of indoor localization, making it viable for real-world applications.