Abstract
The amplitude information (AI) of echoed signals plays an important role in radar target detection and tracking. A lot of research shows that the introduction of AI enables the tracking algorithm to distinguish targets from clutter better and then improves the performance of data association. The current AI-aided tracking algorithms only consider the signal amplitude in the range-azimuth cell where measurement exists. However, since radar echoes always contain backscattered signals from multiple cells, the useful information of neighboring cells would be lost if directly applying those existing methods. In order to solve this issue, a new δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is proposed. It exploits the AI of radar echoes from neighboring cells to construct a united amplitude likelihood ratio, and then plugs it into the update process and the measurement-track assignment cost matrix of the δ-GLMB filter. Simulation results show that the proposed approach has better performance in target’s state and number estimation than that of the δ-GLMB only using single-cell AI in low signal-to-clutter-ratio (SCR) environment.
Highlights
The multi-target tracking (MTT) [1,2] is an important capability for modern radar data processing.Taking the pulsed radar as an example, the pulse compression and clutter suppression are first used to remove clutter and jamming signals from received echoes
Based on the above analysis, this paper proposes a new δ-generalized labeled multi-Bernoulli (δ-GLMB) filter with united likelihood ratio of amplitude information (AI) in neighboring cells in the complex Gaussian distributed clutter
GLMB may generate some false tracks due to a poorer association performance, and these tracks may be close to the actual ones of targets in space, which results in a smaller estimation error
Summary
The multi-target tracking (MTT) [1,2] is an important capability for modern radar data processing. The early implementations of RFS paradigm, such as probability hypothesis density (PHD) [6], cardinalized probability hypothesis density (CPHD) [7] and Multi-Bernoulli [8] filters, ignore the data association in order to reduce the computational complexity This disadvantage leads to the fact that TBD is currently difficult to replace the traditional detection-and-tracking procedure in real-time radar data processing It outperforms the traditional procedure in low signal-to-clutter-ratio (SCR) conditions due to the full use of amplitude difference between target and clutter. In [17], the detection process of echoed signals with slow Rayleigh fluctuation in narrowband Gaussian background is analyzed It incorporates the likelihood ratio of amplitude into the calculation of measurement-track association weights for improving performance of the tracking filter.
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.
