Improved Kalman filter and its application in initial alignment

Wang, Wei; He, Naibao; Yao, Ke-Thia; Tong, Jinwu

doi:10.1016/j.ijleo.2020.165747

Cited by 8 publications

(4 citation statements)

References 29 publications

(17 reference statements)

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“…A time-varying fading factor is used to fade stale data. The covariance matrix of state prediction is adjusted in real time to determine the fading factor, and the convergence condition is set to obtain the depth by filtering 20 – 23 …”

Section: Basic Theory Of Traditional Adaptive Fading Kalman Estimatorsmentioning

confidence: 99%

“…The covariance matrix of state prediction is adjusted in real time to determine the fading factor, and the convergence condition is set to obtain the depth by filtering. [20][21][22][23] Specifically, by introducing a fading factor into the adaptive Kalman filter, the gain matrix of the filter is adjusted online and in real time, so that the residual signals at different times are always orthogonal. In other words, the autocorrelation function of the new information matrix is equal to zero, and the obtained gain matrix is the optimal gain matrix.…”

Section: Basic Theory Of Traditional Adaptive Fading Kalman Estimatorsmentioning

confidence: 99%

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Adaptive fading Kalman depth estimation with chi-square hypothesis testing for Geiger-mode avalanche photo diode LIDAR

et al. 2023

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.The operation of a Geiger-mode avalanche photo diode (GM-APD) LIDAR is severely disturbed by background noise, which makes it challenging to rapidly and accurately estimate the target depth. Therefore, we propose an adaptive fading Kalman depth estimation technique based on chi-square hypothesis testing for GM-APD LIDARs. First, by analyzing the consistency of the echo photon distribution between adjacent pixels of the GM-APD, the pixels are fused to rapidly obtain the echo data of the target surface, thereby accelerating the depth estimation process. Second, we design a chi-square hypothesis test condition based on the statistical characteristics of the innovation vector, which can help evaluate whether the fading factor is introduced at the current moment, promote the convergence of the algorithm, and reduce the depth estimation time. Third, we propose a fading memory index weighted method to adaptively adjust the weight of the observed values to accurately estimate the innovation matrix covariance and determine the optimal fading factor. We demonstrate the effectiveness of the proposed algorithm through simulations and experiments. The results show that the proposed algorithm can rapidly and accurately estimate the target depth in the presence of strong background noise.

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Section: Basic Theory Of Traditional Adaptive Fading Kalman Estimatorsmentioning

confidence: 99%

Section: Basic Theory Of Traditional Adaptive Fading Kalman Estimatorsmentioning

confidence: 99%

Adaptive fading Kalman depth estimation with chi-square hypothesis testing for Geiger-mode avalanche photo diode LIDAR

et al. 2023

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“…In order to improve the accuracy of noise covariance matrix measurement, Wang Wei et al proposed an improved Kalman filter algorithm. which was simulated by initial alignment and compared with the conventional Kalman filter, the error accuracy of the misalignment angle is improved by an order of magnitude [26]. In a paper, Xue Renzheng mentioned the improved frame difference method to detect automatically the moving object's automatic detection and tracking algorithm in a quick a precise way [27].…”

Section: Related Workmentioning

confidence: 99%

Research on detecting moving targets with an improved Kalman filter algorithm

2023

KSII TIIS

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As science and technology evolve, object detection of moving objects has been widely used in the context of machine learning and artificial intelligence. Traditional moving object detection algorithms, however, are characterized by relatively poor real-time performance and low accuracy in detecting moving objects. To tackle this issue, this manuscript proposes a modified Kalman filter algorithm, which aims to expand the equations of the system with the Taylor series first, ignoring the higher order terms of the second order and above, when the nonlinear system is close to the linear form, then it uses standard Kalman filter algorithms to measure the situation of the system. which can not only detect moving objects accurately but also has better real-time performance and can be employed to predict the trajectory of moving objects. Meanwhile, the accuracy and real-time performance of the algorithm were experimentally verified.

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“…To optimally produce a boundary box estimate in the presence of noise and outliers, a KF [32] was employed. For the sake of brevity we omit some details but the reader is encouraged to read [5] for further information on the KF for computer vision applications.…”

Section: Kalman Filter (Kf)mentioning

confidence: 99%

Automated Tackle Injury Risk Assessment in Contact-Based Sports -- A Rugby Union Example

Martin¹,

Patel²,

Hendricks³

2021

Preprint

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Video analysis in tackle-collision based sports is highly subjective and exposed to bias, which is inherent in human observation, especially under time constraints. This limitation of match analysis in tackle-collision based sports can be seen as an opportunity for computer vision applications. Objectively tracking, detecting and recognising an athlete's movements and actions during match play from a distance using video, along with our improved understanding of injury aetiology and skill execution will enhance our understanding how injury occurs, assist match day injury management, reduce referee subjectivity. In this paper, we present a system of objectively evaluating in-game tackle risk in rugby union matches. First, a ball detection model is trained using the You Only Look Once (YOLO) framework, these detections are then tracked by a Kalman Filter (KF). Following this, a separate YOLO model is used to detect persons/players within a tackle segment and then the ballcarrier and tackler are identified. Subsequently, we utilize OpenPose to determine the pose of ball-carrier and tackle, the relative pose of these is then used to evaluate the risk of the tackle. We tested the system on a diverse collection of rugby tackles and achieved an evaluation accuracy of 62.50%. These results will enable referees in tackle-contact based sports to make more subjective decisions, ultimately making these sports safer.

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Improved Kalman filter and its application in initial alignment

Cited by 8 publications

References 29 publications

Adaptive fading Kalman depth estimation with chi-square hypothesis testing for Geiger-mode avalanche photo diode LIDAR

Adaptive fading Kalman depth estimation with chi-square hypothesis testing for Geiger-mode avalanche photo diode LIDAR

Research on detecting moving targets with an improved Kalman filter algorithm

Automated Tackle Injury Risk Assessment in Contact-Based Sports -- A Rugby Union Example

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