2009
DOI: 10.1117/12.831218
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Unbiased Kalman filter using converted measurements: revisit

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Cited by 33 publications
(27 citation statements)
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“…The solution is to replace the true state by the state estimates at time step k from the CPMKF, which uses the converted measurements constructed from the range and azimuth measurements. In this case, the whiteness of the noise part in (15) still can be satisfied in this case, and the covariance of disturbance can be determined using (17). Moreover, since the state error of the CPMKF at time k is independent with the process noise at time k, υ k x and υ k x , the replacement will not lead to correlation between the outputs of the CDMKF and CPMKF.…”
Section: Pseudo-state Equation For Constant Turn Motionmentioning
confidence: 94%
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“…The solution is to replace the true state by the state estimates at time step k from the CPMKF, which uses the converted measurements constructed from the range and azimuth measurements. In this case, the whiteness of the noise part in (15) still can be satisfied in this case, and the covariance of disturbance can be determined using (17). Moreover, since the state error of the CPMKF at time k is independent with the process noise at time k, υ k x and υ k x , the replacement will not lead to correlation between the outputs of the CDMKF and CPMKF.…”
Section: Pseudo-state Equation For Constant Turn Motionmentioning
confidence: 94%
“…When only the position measurements are considered, the converted position measurement Kalman filter (CPMKF) [13,32], which can provide satisfactory performance with the converted measurement errors properly calculated and compensated [5,17,13,19,22,27], is preferable. Due to the high nonlinearity of the Doppler measurements with respect to Cartesian states, difficulties exist in extracting Cartesian states from Doppler measurements.…”
Section: Introductionmentioning
confidence: 99%
“…(Evaluation at the measurement results in correlation between the converted measurement error covariance and converted measurement error itself. This leads to a biased Kalman gain and introduces estimation bias [10], [7], [5]. Therefore, evaluation at the predicted estimate is preferred.)…”
Section: B Estimation Of the Covariancementioning
confidence: 97%
“…For related unbiased conversion methods, the readers may refer to [15,16]. The target is tracked by using both a Kalman Filter (KF) and an Interacting Multiple-Model (IMM) filter.…”
Section: Numerical Examplementioning
confidence: 99%