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Simultaneous State and Parameter Estimation Methods Based on Kalman Filters and Luenberger Observers: A Tutorial & Review [PDF]
SensorsSimultaneous state and parameter estimation is essential for control system design and dynamic modeling of physical systems. This capability provides critical real-time insight into system behavior, supports the discovery of underlying mechanisms, and ...
Amal Chebbi +2 more
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Adaptive Kernel Kalman Filter [PDF]
IEEE Transactions on Signal Processing, 2021 Sequential Bayesian filters in non-linear dynamic systems require the recursive estimation of the predictive and posterior distributions. This paper introduces a Bayesian filter called the adaptive kernel Kalman filter (AKKF). With this filter, the arbitrary predictive and posterior distributions of hidden states are approximated using the empirical ...
Mengwei Sun +3 more
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Comparative study of state and unknown input estimation for continuous–discrete stochastic systems
Franklin Open, 2022 Joint state and unknown input estimation for continuous–discrete stochastic systems can be classified into two types: with and without modeling of unknown inputs.
Peng Lu
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IET Radar, Sonar & Navigation, 2023 The existing adaptive Kalman filters for tracking manoeuvring targets by wireless sensor networks can easily lose robustness when both the measurement and process noises are unknown and time‐varying, resulting in large positioning errors.
Xuming Fang, Dandan Huang
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Design of Adaptive Kalman Consensus Filters (a-KCF)
Signals, 2023 This paper addresses the problem of designing an adaptive Kalman consensus filter (a-KCF) which embedded in multiple mobile agents that are distributed in a 2D domain.
Shalin Ye, Shufan Wu
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IET Communications, 2023 Due to the federal Kalman filter is used to directly fuse the measurement information into the main filter without processing, resulting in the problem of reduced filtering accuracy.
Yi Gao +4 more
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Robust self-adaptive Kalman filter with application in target tracking
Measurement + Control, 2022 Kalman filter has been applied extensively to the target tracking. The estimation performance of Kalman filter is closely resulted by the quality of prior information about the process noise covariance (Q) and the measurement noise covariance (R ...
Yi-Wei Chen, Ken-Ming Tu
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Recursive Optimal Finite Impulse Response Filter and Its Application to Adaptive Estimation
Applied Sciences, 2022 In this paper, the recursive form of an optimal finite impulse response filter is proposed for discrete time-varying state-space models. The recursive form of the finite impulse response filter is derived by employing finite horizon Kalman filtering with
Bokyu Kwon, Sang-il Kim
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The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods
Sensors, 2021 State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time.
Xue-Bo Jin +4 more
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Partial Diffusion Kalman Filter With Adaptive Combiners [PDF]
IEEE Transactions on Aerospace and Electronic Systems, 2021 Adaptive estimation of optimal combination weights for partial-diffusion Kalman filtering together with its mean convergence and stability analysis is proposed here. The simulations confirm its superior performance compared with the existing combiners. Sensor networks with limited accessible power highly benefit from this design.
Azam Khalili +4 more
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