Abstract
In surveillance applications, the extent states and measurements of extended targets received by sensors are time-varying. In this paper, we propose a joint tracking and classification (JTC) method for single extended target under the presence of clutter and detection uncertainty. The extent state is modeled as elliptic shape via random matrix model (RMM), and is used as the feature for target classification. To adapt to the time-varying conditions of an extended target, the RMM proposed by Lan et al. is used. Besides, the RMM is integrated into Bernoulli filter to detect an extended target with clutter and detection uncertainty. The resulting method is called joint tracking and classification Gaussian inverse Wishart Bernoulli (JTC-GIW-Ber) filter, and the closed expressions for JTC-GIW-Ber filter recursions are derived under the necessary assumptions and approximations. Comprehensive simulations are carried out to test the performance, and the results demonstrate that the proposed JTC-GIW-Ber filter not only outperforms the JTC-GIW probability hypothesis density (JTC-GIW-PHD) filter and the GIW-Ber filter in extent state estimation, but also outperforms the JTC-GIW-PHD filter in target classification.
Highlights
In many civil and military applications such as surveillance, target tracking and target classification are two critical problems
We qualitatively evaluate the performance of the proposed joint tracking and classification (JTC)-GIW-Ber filter, the GIW-Ber filter, and the JTC-GIW-probability hypothesis density (PHD) filter
These indicate that the proposed JTC-GIW-Ber filter can achieve better performance in estimating time-varying extent state compared with the GIW-Ber filter
Summary
In many civil and military applications such as surveillance, target tracking and target classification are two critical problems. In the real application environment, the classification feature originated from extended target is probably time-varying, and needs to be modeled as extent state in the estimate process. These previous methods did not address the issues of tracking multiple extended targets with unknown number, or with clutter and detection uncertainty To solve these problems, Hu et al [19] proposed a JTC method for maneuvering extended multi-target based on probability hypothesis density (PHD) filter [20] and RMM. The JTC methods of extended targets based on the extent state have better robustness and need no extra sensor measurement information. We propose a joint tracking and classification Gaussian inverse Wishart Bernoulli (JTC-GIW-Ber) filter by using the extent state as the feature for classification.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
