Robust self-adaptive Kalman filter with application in target tracking

Chen, Yi-Wei; Tu, Ken-Ming

doi:10.1177/00202940221083548

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…The Kalman filter (KF) is a technique utilized to estimate a value, specifically the linear least mean squares estimator (LLSME). It improves system performance by utilizing the statistical properties of noise and an accurate dynamic model of the system [15]. The EKF is a derivative of the KF designed to estimate the state of the system.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter

Ath Thahirah Al Azhima,

Hakim,

Nulfatwa

et al. 2024

IJEECS

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Autonomous robots have gained significant attention in research due to their ability to facilitate human work. Navigation systems, particularly localization, present a challenge in autonomous robots. The inertial navigation system is a localization system that uses inertial sensors and a wheel odometer to estimate the robot’s relative position to the initial position. However, the system is susceptible to continuous error accumulation over time due to factors like sensor noise and wheel slip. To address these issues, external sensors are required to measure the robot’s position in the environment. The extended Kalman filter (EKF) method is utilized to estimate the robot’s position based on wheel odometer and light detection and ranging (LIDAR) sensor measurements. In the prediction stage, the input to the EKF is the position measurement from the wheel odometer, while the LIDAR sensor’s position measurement is used in the update stage to improve the prediction stage results. The test results reveal that the EKF’s estimated position has a lower average error compared to the position measurement using the wheel odometer. Therefore, it can be concluded that the EKF technique is effectively applied to the robot and can correct the wheel odometer's cumulative error with the assistance of the LIDAR sensor.

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Section: The Proposed Methodsmentioning

confidence: 99%

Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter

Ath Thahirah Al Azhima,

Hakim,

Nulfatwa

et al. 2024

IJEECS

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“…We adopt the naming convention used in previous studies [45], [46] that refer to the Kalman Filter as adaptive when the covariance matrices Q and R are dynamically modified. Accordingly, we introduce our approach as the Active-Passive Two-Way Ranging Adaptive Extended Kalman Filter (AP-TWR A-EKF) positioning method.…”

Section: E Proposed Adaptive Ekf Methodsmentioning

confidence: 99%

Adaptive Extended Kalman Filter Position Estimation Based on Ultra-Wideband Active-Passive Ranging Protocol

Laadung,

Ulp,

Fjodorov

et al. 2023

IEEE Access

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This paper first presents a comprehensive analysis of Non-Line-of-Sight (NLoS) error cases in the Ultra-Wideband (UWB) Active-Passive Two-Way Ranging (AP-TWR) protocol. Based on this analysis, we then propose the Adaptive Extended Kalman Filter (A-EKF) positioning method, utilizing variances calculated from AP-TWR range estimates, which are adapted based on the distance and intermittency of the range estimates. The proposed method needs no training data, nor any additional information about the environment the system is deployed in and does not yield any additional time delays. Based on experiments conducted in an industrial environment, the results show that the proposed method outperforms standard non-adaptive AP-TWR and active-only Single-Sided Two-Way Ranging (SS-TWR) methods in both stationary and movement tests. The stationary tests show that on average the proposed A-EKF method provides more than three times lower Root-Mean-Square-Error (RMSE) than the next best method (AP-TWR) in 3D positioning, while SS-TWR consistently performs worse by about 0.4 m in the z-axis. Additionally, the movement tests confirm the findings of the stationary tests and show that the challenging propagation conditions of the testing environment cause maximum errors at about 4.5 m for AP-TWR and SS-TWR, whereas the proposed A-EKF managed to mitigate these effects and reduce the error by 9 times, resulting in a maximum error of 0.5 m.

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“…Computer vision is a comprehensive and interdisciplinary discipline involving numerous aspects such as image processing, intelligent pattern recognition, artificial intelligence, automatic control, and neural networks [7]. Research in computer vision aims to enable computers to sense and understand the external environment, so that they can simulate human vision [8]. Motion target tracking is an important topic in the field of computer vision [9], which focuses on detecting, locating and tracking targets in video frames, obtaining the motion characteristics of the targets, and further processing and analyzing them to achieve higher-level tasks [10].…”

Section: Introductionmentioning

confidence: 99%

Kalman Filter-based Signal Processing for Robot Target Tracking

Gong¹

2023

IJACSA

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In the field of computer vision, the signal tracking of moving objects is a highly representative problem. Therefore, how to accurately and quickly track the target unit has become the focus of the research. Based on this, a Cam Shift algorithm improved by Kalman filtering algorithm is introduced to realize fast tracking of moving targets. This method uses the prediction function of the Kalman filter to predict the moving target of the next frame, transforms the global search problem into a local search problem, and improves the real-time performance. The experimental results show that, in the case of complete occlusion, the trajectory of the unimproved algorithm will deviate compared with the actual trajectory of the improved trajectory tracking curve, but the improved algorithm has no trajectory deviation. The error of the improved algorithm is about 4%, while the maximum error of the unimproved algorithm is about 90%. The improved algorithm reached the expected target accuracy after 110 and 78 trainings in X and Y coordinates, respectively, while the CamShift algorithm without Kalman filtering still failed to reach the expected error after 200 trainings in X and Y coordinates. This indicates that the performance of the improved CamShift algorithm based on Kalman filter has been greatly improved. In conclusion, the improved algorithm proposed in this study is highly practical.

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Robust self-adaptive Kalman filter with application in target tracking

Cited by 6 publications

References 24 publications

Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter

Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter

Adaptive Extended Kalman Filter Position Estimation Based on Ultra-Wideband Active-Passive Ranging Protocol

Kalman Filter-based Signal Processing for Robot Target Tracking

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