An Improved Extended Kalman Filter for Radar Tracking of Satellite Trajectories

Coelho, Milca de Freitas; Bousson, Kouamana; Ahmed, Kawser

doi:10.3390/designs5030054

Cited by 7 publications

(5 citation statements)

References 35 publications

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“…Kalman filtering is one of the Bayesian filtering approaches commonly used for state estimation, the problem of which consists of estimating the state vector x that describes the true behaviours of a dynamical system based on limited and noisy observations z which are often imperfect or even unknown due to the inaccuracy of sensor measurements and the restrictions of data acquisition [22]. The Kalman filter (KF) has been applied to many engineering applications such as target tracking [28], navigation [29] and system identification [30].…”

Section: Extended Kalman Filtermentioning

confidence: 99%

“…Recently, stochastic processes [14,15] and recursive Bayesian filters [16,17] have demonstrated promising performance in RUL estimation and offer a flexible framework for incorporating prior knowledge and updating beliefs based on new observations. The Kalman filter (KF) and its variants [18,19], the extended Kalman filter (EKF) [20] and unscented Kalman filter (UKF) [21], are efficient state estimation tools for dynamical systems given inaccurate models and noisy observations and have been frequently utilized in various industrial applications, such as the tracking of satellite trajectories [22] and the positioning of autonomous vehicles [23]. In the case of bearing condition monitoring, the latent degradation level is the target of state estimation behind the observable CM measurements, such as vibration acceleration signals, or the bearing health indicators (HI) obtained from certain feature engineering techniques.…”

Section: Introductionmentioning

confidence: 99%

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Early Prediction of Remaining Useful Life for Rolling Bearings Based on Envelope Spectral Indicator and Bayesian Filter

Wen,

Zhang,

Sinha

2024

Applied Sciences

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On top of the condition-based maintenance (CBM) practice for rotating machinery, the robust estimation of remaining useful life (RUL) for rolling-element bearings (REB) is of particular interest. The failure of a single bearing often results in secondary defects in the connected structure and catastrophic system failures. The prediction of RUL facilitates proactive maintenance planning to ensure system reliability and minimize financial loss due to unscheduled downtime. In this paper, to acquire early and reliable estimations of useful life, the RUL prediction of REBs is formulated into nonlinear degradation state estimation tackled by the combination of the envelope spectral indicator (ESI) and extended Kalman filter (EKF). By fusing the spectral energy of the bearing fault characteristic frequencies (FCFs) in the averaged envelope spectrum, the ESI is crafted to remove the interference from rotor-dynamics and reveal the bearing deterioration process. Once the fault is identified, the recursive Bayesian method based on EKF is utilized for estimating the bearing end-of-life time via the exponential state-space model. The distinctive advantage of the proposed approach lies in its ability to make an early prediction of RUL using a small number of ESI observations, offering an efficient practice for predictive health management at the early stage of bearing fault. The performance of the proposed method is validated using publicly available experimental bearing vibration data across three different operating conditions.

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Section: Extended Kalman Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Early Prediction of Remaining Useful Life for Rolling Bearings Based on Envelope Spectral Indicator and Bayesian Filter

Wen,

Zhang,

Sinha

2024

Applied Sciences

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“…Optimal sequential estimation for linearized orbital dynamics in Minimum Mean Square Error (MMSE) sense is provided by Linearized Kalman Filter (KF) [1],[2], [3]. Proficient nonlinear filtering algorithms such as Extended Kalman Filter (EKF) and improved EKF (iEKF) were derived based on Gaussian assumption of Bayes' posterior PDF and availability of rich measurement environment [1],[2], [3], [4], [5], [6]. However, majority of the aerospace systems are nonlinear which consider non-Gaussian evolution of state, for example tracking of an Exo-atmospheric Re-Entry Vehicle (ERV), space object tracking, navigation of robots or aircrafts [7], [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Mixture Culver Filter for Space Object Orbit Determination

Gilani¹,

Mihaylova²,

Bhutta³

2023

Preprint

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Exact sequential state estimation of orbiting objects in space can be found by predicting the state using Fokker Planck Kolmogorov Equation (FPKE) and measurement correction using Bayes’ conditional/posterior Probability Density Function (PDF). Posterior PDF can be expressed as Gaussian density expanded in terms of Hermite polynomials named as Gram Charlier Series (GCS). This research is an extension of an earlier work on second order linearized solution to nonlinear Bayesian filtering using Taylor series and third order GCS expansion of posterior PDF. In this new extension, Bayes’ posterior PDF is approximated by a mixture of GCS functions for which the parameters are propagated using linear propagation theory. The update of weights of different components of GCS mixture model uses the FPKE error as feedback to adapt for the amplitude of different GCS components while solving a quadratic programming problem earlier used for Gaussian Mixture Model (GMM) PDF. Proposed filtering method is applied on tracking of space debris. The simulation results for the filter shows performance is moderately better than single GCS filter, Extended Kalman Filter (EKF) and Gaussian Sum Filter (GSF) under space debris’ highly uncertain initial conditions and sparse measurement availability.

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“…This Special Issue on the "Unmanned Aerial System (UAS) Modeling, Simulation and Control-Part I" focuses on publishing original manuscripts and literature review papers in the areas of UAS modeling, simulation, robust control, artificial intelligent control, design, aerodynamics, aeroelasticity, morphing systems, trajectory optimization, flight tests, wind tunnel tests and other areas closely related to UAS technology improvement. This Special Issue presents research on various UASs and other systems, including the UAS-S45 from the Mexican company Hydra Technologies [1], quadrotors [2,3], drone collision avoidance systems [4], Remotely Piloted Aircraft Systems (RPASs) in [5] and satellite trajectories tracking by radar [6].…”

mentioning

confidence: 99%

“…In ''An Improved Extended Kalman Filter for Radar Tracking of Satellite Trajectories" [6], an improved Extended Kalman Filter (iEKF) method was successfully validated in a realistic simulation of satellite orbit estimation and its transfer. The iEKF method is an improved version of the classical Extended Kalman Filter (EKF), which has many limitations, including poor convergence, erratic behaviors, or inadequate linearization when applied to highly nonlinear systems.…”

mentioning

confidence: 99%