Add to ORKGA fuzzy Kalman filter algorithm is developed for target tracking applications and its performance evaluated using several numerical examples. The approach is relatively novel. A comparison with Kalman filter and an adaptive tuning algorithm is carried out. The applicability13; and usefulness of fuzzy logic in data fusion is also demonstrated. The performance of both the extended Kalman filter and fuzzy extended Kalman filter is evaluated using real data of a manoeuvering target and it is found that fuzzy extended Kalman filter shows better performance as compared to extended Kalman filter
Abstract − In this paper a development of an adaptive Kalman filter through a fuzzy inference system...
An attempt has been made in this thesis to overcome some of the limitations associated with the Kalm...
Abstract: Aiming at the multi-source heterogeneous of target tracking system information. On the bas...
A fuzzy Kalman filter algorithm is developed for target tracking applications and itsperformance eva...
The applications of Kalman filter include tracking, navigation, guidance, control and parameter esti...
The Kalman filter is globally accepted by estimation community and frequently applied in many real:t...
The Kalman filter provides an effective means of estimating the state of a system from noisy measure...
This research work concerns the development of novel adaptive multi-sensor data fusion (MSDF) archit...
In this paper Kalman filter and Gain fusion based multi-sensor data13; fusion algorithms are investi...
The main problem that appears with tracking high-performance targets is the severe random change in ...
Four methods of process noise covariance tuning in a Kalman filter are evaluated. The methods studie...
In the engineering field measurement systems that can provide the most accurate results are increasi...
Vita.In this research, fuzzy processing is applied to the adaptive Kalman filter. The filter gain c...
In this paper factorization filtering, fusion filtering strategy and related algorithms are presente...
Fuzzy logic based systems are known for performing tasks to certain predefined\ud precision level. T...
Abstract − In this paper a development of an adaptive Kalman filter through a fuzzy inference system...
An attempt has been made in this thesis to overcome some of the limitations associated with the Kalm...
Abstract: Aiming at the multi-source heterogeneous of target tracking system information. On the bas...
A fuzzy Kalman filter algorithm is developed for target tracking applications and itsperformance eva...
The applications of Kalman filter include tracking, navigation, guidance, control and parameter esti...
The Kalman filter is globally accepted by estimation community and frequently applied in many real:t...
The Kalman filter provides an effective means of estimating the state of a system from noisy measure...
This research work concerns the development of novel adaptive multi-sensor data fusion (MSDF) archit...
In this paper Kalman filter and Gain fusion based multi-sensor data13; fusion algorithms are investi...
The main problem that appears with tracking high-performance targets is the severe random change in ...
Four methods of process noise covariance tuning in a Kalman filter are evaluated. The methods studie...
In the engineering field measurement systems that can provide the most accurate results are increasi...
Vita.In this research, fuzzy processing is applied to the adaptive Kalman filter. The filter gain c...
In this paper factorization filtering, fusion filtering strategy and related algorithms are presente...
Fuzzy logic based systems are known for performing tasks to certain predefined\ud precision level. T...
Abstract − In this paper a development of an adaptive Kalman filter through a fuzzy inference system...
An attempt has been made in this thesis to overcome some of the limitations associated with the Kalm...
Abstract: Aiming at the multi-source heterogeneous of target tracking system information. On the bas...