Robust Derivative Unscented Kalman Filter Under Non-Gaussian Noise

Yin, Lijian; Deng, Zhihong; Huo, Bo; Xia, Yuanqing; Li, Cheng

doi:10.1109/access.2018.2846752

Cited by 20 publications

(10 citation statements)

References 33 publications

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“…The filter on its own is limited to a very specific set of agent attributes and requires further augmentation to apply real data to ABMs. Research is being done into the application of the Kalman Filtering to continuous non-Gaussian data [25] and discrete variables [26,27,28] but little is being done [29] on estimating a mixed state of both categorical and Gaussian variables with the UKF. This has been attempted for use in agent-based models using particle filters [e.g.…”

Section: Related Workmentioning

confidence: 99%

Real-time agent-based crowd simulation with the Reversible Jump Unscented Kalman Filter

Clay

Ward

Ternes

et al. 2021

Simulation Modelling Practice and Theory

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Section: Related Workmentioning

confidence: 99%

Real-time agent-based crowd simulation with the Reversible Jump Unscented Kalman Filter

Clay

Ward

Ternes

et al. 2021

Simulation Modelling Practice and Theory

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“…A correction stage is a process of correcting the predicted value [95]. The first step was to calculate the Kalman gain 𝐾 𝑘 (39).…”

Section: -2-correction Stagementioning

confidence: 99%

Using a Combination of PID Control and Kalman Filter to Design of IoT-based Telepresence Self-balancing Robots during COVID-19 Pandemic

et al. 2022

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COVID-19 is a very dangerous respiratory disease that can spread quickly through the air. Doctors, nurses, and medical personnel need protective clothing and are very careful in treating COVID-19 patients to avoid getting infected with the COVID-19 virus. Hence, a medical telepresence robot, which resembles a humanoid robot, is necessary to treat COVID-19 patients. The proposed self-balancing COVID-19 medical telepresence robot is a medical robot that handles COVID-19 patients, which resembles a stand-alone humanoid soccer robot with two wheels that can maneuver freely in hospital hallways. The proposed robot design has some control problems; it requires steady body positioning and is subjected to disturbance. A control method that functions to find the stability value such that the system response can reach the set-point is required to control the robot's stability and repel disturbances; this is known as disturbance rejection control. This study aimed to control the robot using a combination of Proportional-Integral-Derivative (PID) control and a Kalman filter. Mathematical equations were required to obtain a model of the robot's characteristics. The state-space model was derived from the self-balancing robot's mathematical equation. Since a PID control technique was used to keep the robot balanced, this state-space model was converted into a transfer function model. The second Ziegler-Nichols's rule oscillation method was used to tune the PID parameters. The values of the amplifier constants obtained were Kp=31.002, Ki=5.167, and Kd=125.992128. The robot was designed to be able to maintain its balance for more than one hour by using constant tuning, even when an external disturbance is applied to it. Doi: 10.28991/esj-2021-SP1-016 Full Text: PDF

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“…In order to test the effectiveness of PCM-GSF in a high dimensional system, a range-bearing scenario in the [43] is presented. The target follows a constant velocity (CV) model, and the state vector contains the position and velocity in both X and Y directions, respectively.…”

Section: Range-bearing Scenario Analysismentioning

confidence: 99%

An Efficient Gaussian Sum Filter Based on Prune-Cluster-Merge Scheme

et al. 2019

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The main problem for the state estimation with Gaussian mixture model is the exponentially growing number of Gaussian components. To solve this problem, an efficient Gaussian sum filter (GSF) based on the prune-cluster-merge (PCM) scheme-based Gaussian mixture reduction method is proposed. First, an adaptive weight-censored pruning strategy named as the j-th order statistical technique is presented to delete components with little contributes to the posterior distribution. Then, the Gauss clustering method is proposed to partition the remainder components into clusters based on the newly defined distribution similarity criterion. Integrating the acquired clusters with the covariance intersection algorithm, the components in the same cluster are merged into a standard Gaussian component by keeping the shape of the original distribution. Meanwhile, an extended integral square error cost function is constructed to optimize the performance of the cluster-merge operation. Finally, an efficient Gaussian sum filter is derived by combining the PCM scheme with extended Kalman filters. Numerical results show that the proposed filter can not only keep a better approximation to the original distribution with fewer Gaussian components comparing with the number-limited GSF and Runnalls's GSF, but achieve a higher cost-effectiveness than the particle filter.

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Robust Derivative Unscented Kalman Filter Under Non-Gaussian Noise

Cited by 20 publications

References 33 publications

Real-time agent-based crowd simulation with the Reversible Jump Unscented Kalman Filter

Real-time agent-based crowd simulation with the Reversible Jump Unscented Kalman Filter

Using a Combination of PID Control and Kalman Filter to Design of IoT-based Telepresence Self-balancing Robots during COVID-19 Pandemic

An Efficient Gaussian Sum Filter Based on Prune-Cluster-Merge Scheme

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