Optimal tuning of a Kalman filter using genetic algorithms

Oshman, Yaakov; Shaviv, Ilan G.

doi:10.2514/6.2000-4558

Cited by 29 publications

(19 citation statements)

References 1 publication

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“…(34) The differenced measurement given in Eq. (22) and the modi ed matrices de ned in Eqs. (23), (33), and (34) can be used in a standard Kalman lter, where the original dimensions of the state vector, its state transition matrix, and the covariance matrices are preserved.…”

Section: Colored Noise Modelingmentioning

confidence: 99%

Spacecraft Angular Rate Estimation from Magnetometer Data Only Using an Analytic Predictor

Tortora

Oshman

Santoni

2004

Journal of Guidance, Control, and Dynamics

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A method is presented for fast estimation of the angular rate of a tumbling spacecraft in a low-Earth orbit from sequential readings of Earth's magnetic eld. Useful as a backup algorithm in cases of rate gyro malfunctionsor during the initial acquisitionphase, the estimatorconsists of an extended Kalman lter, based on the assumptionthat the inertial geomagnetic eld vector does not signi cantly change during the short sampling time. As the external disturbance torque is neglected, an analytic solution of Euler's equations can be used in the lter's propagation phase, allowing a signi cant savings of computation time compared to numerical integration of Euler's equations. Contrary to most existing angular rate estimators, the spacecraft's attitude is neither used nor estimated within the proposed algorithm. Moreover, the body-referenced geomagnetic eld observations are not differentiated with respect to time as an external pre ltering procedure but are directly processed by the lter. This processing gives rise to a colored effective measurement noise, which is properly handled via approximate Markov modeling and application of Bryson and Henrikson's reduced-order ltering theory. A simulation study employing a standard tenth-order International Geomagnetic Reference Field model is presented to demonstrate the performance of the algorithm.

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Section: Colored Noise Modelingmentioning

confidence: 99%

Spacecraft Angular Rate Estimation from Magnetometer Data Only Using an Analytic Predictor

Tortora

Oshman

Santoni

2004

Journal of Guidance, Control, and Dynamics

Self Cite

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“…The process and measurement noises in the test are modeled as zero‐mean Gaussian variables and mutually independent. The standard deviation of the process noise used in each algorithm is determined by a genetic optimization‐based tuning method proposed in Oshman and Shaviv (2000). More examples of tuning methods can be found in Oshman and Shaviv (2000) and Imtiaz et al.…”

Section: Test Of the Proposed Methodsmentioning

confidence: 99%

“…The standard deviation of the process noise used in each algorithm is determined by a genetic optimization‐based tuning method proposed in Oshman and Shaviv (2000). More examples of tuning methods can be found in Oshman and Shaviv (2000) and Imtiaz et al. (2006).…”

Section: Test Of the Proposed Methodsmentioning

confidence: 99%

Novel prior position determination approaches in particle filter for ultra wideband (UWB)‐based indoor positioning

et al. 2021

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Filtering‐based indoor positioning using ultra wideband (UWB) requires known velocity to predict prior position in the prediction stage. Velocity can be obtained from an inertial measurement unit (IMU) sensor or the posterior state vector at the previous time stamp. Both methods have limitations when using them in practice. This paper proposes two novel velocity determination approaches, which use measurements to approximate velocity in a self‐contained way. They are integrated into particle filtering algorithms for prior position determination. The test result shows that the particle filter with the proposed approaches performs similarly to the Rao‐Blackwellized particle filter and slightly better than the particle filter with IMU. Compared with the standard particle filter, the particle filters with our proposed approaches achieve similar positioning accuracies with less computation time. Moreover, it is found that the integration of Angle‐of‐Arrival measurements in particle‐filter‐based positioning improves the 3‐D positioning accuracy by about 37.3% on average.

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“…In [ 7 ], a method is shown where the filter is considered as a control system, thus allowing corresponding tuning criteria to be derived. In [ 8 ], the filter parameters are computed via optimization based on a genetic algorithm. The authors in [ 9 ] use Bayesian optimization for this purpose.…”

Section: Introductionmentioning

confidence: 99%

A Novel Kalman Filter Design and Analysis Method Considering Observability and Dominance Properties of Measurands Applied to Vehicle State Estimation

Ruggaber

Brembeck

2021

Sensors

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In Kalman filter design, the filter algorithm and prediction model design are the most discussed topics in research. Another fundamental but less investigated issue is the careful selection of measurands and their contribution to the estimation problem. This is often done purely on the basis of empirical values or by experiments. This paper presents a novel holistic method to design and assess Kalman filters in an automated way and to perform their analysis based on quantifiable parameters. The optimal filter parameters are computed with the help of a nonlinear optimization algorithm. To determine and analyze an optimal filter design, two novel quantitative nonlinear observability measures are presented along with a method to quantify the dominance contribution of a measurand to an estimate. As a result, different filter configurations can be specifically investigated and compared with respect to the selection of measurands and their influence on the estimation. An unscented Kalman filter algorithm is used to demonstrate the method’s capabilities to design and analyze the estimation problem parameters. For this purpose, an example of a vehicle state estimation with a focus on the tire-road friction coefficient is used, which represents a challenging problem for classical analysis and filter parameterization.

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Optimal tuning of a Kalman filter using genetic algorithms

Cited by 29 publications

References 1 publication

Spacecraft Angular Rate Estimation from Magnetometer Data Only Using an Analytic Predictor

Spacecraft Angular Rate Estimation from Magnetometer Data Only Using an Analytic Predictor

Novel prior position determination approaches in particle filter for ultra wideband (UWB)‐based indoor positioning

A Novel Kalman Filter Design and Analysis Method Considering Observability and Dominance Properties of Measurands Applied to Vehicle State Estimation

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