An Efficient IAKF Approach for Indoor Positioning Drift Correction

Lin, Shang-Hsien; Chien, Hung-Hsien Chang; Wang, Weiwen; Lin, Kuang-Hao; Li, Guan-Jin

doi:10.3390/s22155697

Cited by 2 publications

(4 citation statements)

References 33 publications

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“…The common methods for achieving relative positioning in GNSS-denied environments can be classified into the following three categories: visual positioning [7][8][9][10][11][12][13][14][15][16][17], inertial navigation positioning [18][19][20][21][22], and radio positioning [23,24]. The development status of each category of positioning technology is systematically discussed below, and the advantages and limitations of each technology are analyzed, providing good inspiration for the study of relative positioning methods for MAVs in GNSS-denied environments.…”

Section: Introductionmentioning

confidence: 99%

“…There are various wireless positioning methods based on radio technology, mainly including infrared positioning [23], Ultra Wide Band (UWB) positioning [24], and radio frequency identification (RFID) positioning [25]. In [23], the angle of an object equipped with a positioning device was measured using an infrared laser beam emitted by a lighthouse base station, and the position of the object was calculated.…”

Section: Introductionmentioning

confidence: 99%

“…In [23], the angle of an object equipped with a positioning device was measured using an infrared laser beam emitted by a lighthouse base station, and the position of the object was calculated. An indoor positioning system based on improved adaptive Kalman filter (IAKF) was proposed in [24] that calculated the distance between the tag and the base stations using the time it took for UWB signals to travel, and then applied a triangulation algorithm to acquire the position of the tag. Similar to [24], reference [25] differs mainly in the method of distance measurement, using RFID technology to calculate the distance.…”

Section: Introductionmentioning

confidence: 99%

“…An indoor positioning system based on improved adaptive Kalman filter (IAKF) was proposed in [24] that calculated the distance between the tag and the base stations using the time it took for UWB signals to travel, and then applied a triangulation algorithm to acquire the position of the tag. Similar to [24], reference [25] differs mainly in the method of distance measurement, using RFID technology to calculate the distance. In addition, Ref.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied Environments

Cheng

Ren

Deng

2023

Sensors

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Global Navigation Satellite Systems (GNSS) with weak anti-jamming capability are vulnerable to intentional or unintentional interference, resulting in difficulty providing continuous, reliable, and accurate positioning information in complex environments. Especially in GNSS-denied environments, relying solely on the onboard Inertial Measurement Unit (IMU) of the Micro Aerial Vehicles (MAVs) for positioning is not practical. In this paper, we propose a novel cooperative relative positioning method for MAVs in GNSS-denied scenarios. Specifically, the system model framework is first constructed, and then the Extended Kalman Filter (EKF) algorithm, which is introduced for its ability to handle nonlinear systems, is employed to fuse inter-vehicle ranging and onboard IMU information, achieving joint position estimation of the MAVs. The proposed method mainly addresses the problem of error accumulation in the IMU and exhibits high accuracy and robustness. Additionally, the method is capable of achieving relative positioning without requiring an accurate reference anchor. The system observability conditions are theoretically derived, which means the system positioning accuracy can be guaranteed when the system satisfies the observability conditions. The results further demonstrate the validity of the system observability conditions and investigate the impact of varying ranging errors on the positioning accuracy and stability. The proposed method achieves a positioning accuracy of approximately 0.55 m, which is about 3.89 times higher than that of an existing positioning method.

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