Critical remarks on the linearised and extended Kalman filters with geodetic navigation examples

Jwo, Dah‐Jing; Cho, Ta-Shun

doi:10.1016/j.measurement.2010.05.008

Cited by 34 publications

(13 citation statements)

References 13 publications

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“…Because the parameters of the state vector x kin have nonlinear relations, the extended Kalman filter (EKF) is used in this study with the total formulation [20]. Therefore, the state transition matrix Φ k in k-th step is defined as:…”

Section: Process Modelmentioning

confidence: 99%

Minimization of the positional errors for an accurate determination of the kinematic parameters of a rigid-body system with miniature inertial sensors

Žumer

Slavič

Boltežar

2014

Mechanism and Machine Theory

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Section: Process Modelmentioning

confidence: 99%

Minimization of the positional errors for an accurate determination of the kinematic parameters of a rigid-body system with miniature inertial sensors

Žumer

Slavič

Boltežar

2014

Mechanism and Machine Theory

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“…We assume that there are N beacon nodes in the field. The state equation of sensor i can be expressed as follows [12][13]:…”

Section: Extended Kalman Based Mobile Location 31 Extended Kalman Fmentioning

confidence: 99%

RSSI-based Indoor Mobile Localization in Wireless Sensor Network

Chen¹

2011

JDCTA

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Location techniques play an important part in wireless sensor network. The received signal strength indicator (RSSI) based localization is a promising technique since it requires relatively low configuration and energy. The received signal strength is mainly influenced by propagation environment in wireless sensor network. So we proposed an indoor mobile location algorithm (IMLA). We firstly introduce a signal propagation model. And then we employ the least squares and maximum likelihood estimation to estimate the parameters of signal model. Finally the extended Kalman filter is used to filter the RSSI values and convert the measured RSS value to distance. And the Cramer-Rao bound for RSSI-based location estimation is expressed. Simulation results show that the proposed IMLA outperforms the maximum likelihood location and non-filter mobile location algorithm with better location estimation accuracy.

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“…Rawicz et al reported in [20] explicit Kalman-Bucy-like solutions for twostate H 1 and H 2 filters. In [21], Jwo and Cho have made several useful remarks regarding the linearized and extended Kalman filters, and we meet new useful solutions each year.…”

Section: Introductionmentioning

confidence: 99%

Ultimate iterative UFIR filtering algorithm

2016

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Measurements are often provided in the presence of noise and uncertainties that require optimal filters to estimate processes with highest accuracy. The ultimate iterative unbiased finite impulse response (UFIR) filtering algorithm presented in this paper is more robust in real world than the Kalman filter. It completely ignores the noise statistics and initial values while demonstrating better accuracy under the mismodeling and temporary uncertainties and lower sensitivity to errors in the noise statistics.

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Critical remarks on the linearised and extended Kalman filters with geodetic navigation examples

Cited by 34 publications

References 13 publications

Minimization of the positional errors for an accurate determination of the kinematic parameters of a rigid-body system with miniature inertial sensors

Minimization of the positional errors for an accurate determination of the kinematic parameters of a rigid-body system with miniature inertial sensors

RSSI-based Indoor Mobile Localization in Wireless Sensor Network

Ultimate iterative UFIR filtering algorithm

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