Multi-Bernoulli filtering with unknown clutter intensity and sensor field-of-view
MetadataShow full item record
In Bayesian multi-target filtering knowledge of parameters such as clutter intensity and sensor field-of-view are of critical importance. Significant mismatches in clutter and sensor field of view model parameters results in biased estimates. In this paper we propose a multi-target filtering solution that can accommodate non-linear target model and unknown non-homogeneous clutter intensity and sensor field-of-view. Our solution is based on the multi-target multi-Bernoulli filter that adaptively learns non-homogeneous clutter intensity and sensor field-of-view while filtering. © 2011 IEEE.
Showing items related by title, author, creator and subject.
Gostar, A.; Hoseinnezhad, R.; Bab-Hadiashar, A.; Vo, Ba Tuong (2013)This paper builds on the recently developed adaptive multi-Bernoulli filter, proposing a novel sensor control solution within the multi-object filtering scheme. Our sensor control method does not need any prior information ...
Kim, Du Yong; Jeon, M. (2015)To achieve reliable multi-object filtering in vision application, it is of great importance to determine appropriate model parameters. Parameters such as motion and measurement noise covariance can be chosen based on the ...
Vo, Ba Tuong; Vo, Ba-Ngu; Hoseinnezhad, R.; Mahler, R. (2013)In Bayesian multi-target filtering knowledge of parameters such as clutter intensity and detection probability profile are of critical importance. Significant mismatches in clutter and detection model parameters results ...