An Adaptive UKF Algorithm for Single Observer Passive Location in Non-Gaussian 
 Environment

Song, Huihui; Fu, Yong Qing; Liu, Xue

doi:10.3923/itj.2012.1251.1257

Cited by 2 publications

(1 citation statement)

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“…After state estimation of the above nonlinear system, the linear approximation in EKF (Extended Kalman Filter, EKF) algorithm was replaced by UT [12], and then the UKF [13] algorithm for nonlinear system state estimation was acquired [14]. Now simply after sigma point sampling of the system state vector, the process noise and measurement noise can be separated.…”

Section: Ukf (Unscented Kalman Filter)mentioning

confidence: 99%

Improved Different Dimensional Sensors Combined Space Registration Algorithm

Chen

Yin

et al. 2015

Mathematical Problems in Engineering

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To address the problem of deviation and registration of 3D radar and infrared sensor, this paper presents and improves a method based on the state value and space deviation of federated filtering of unscented Kalman filter and standard Kalman filter, which conduces to real time registering of system deviation of radar and IF sensors. In the method presented here, a covariance matching criteria-based approach was employed for judgment of filtering divergent trend, while self-adaptive attenuation factor was introduced for correction of the predicted error covariance so as to inhibit the divergent phenomenon. The experiment results indicated that the method presented here conduces to improvement of the precision and speed of space registration, showing practical value in deviation registration of 3D radars and infrared sensors.

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Section: Ukf (Unscented Kalman Filter)mentioning

confidence: 99%