A Sequential Detection and Tracking of Very Low SNR Objects
This work addresses a domain-specific challenge in signal processing or radar systems, but appears incremental as it builds on existing methods with comparative improvements.
The paper tackles the problem of detecting and tracking objects with very low signal-to-noise ratio (SNR) by proposing a sequential detection and tracking (SDT) approach, which outperforms existing particle filter track-before-track methods in simulations.
A sequential detection and tracking (SDT) approach is proposed for detection and tracking of very low signal-to-noise (SNR) objects. The proposed approach is compared with two existing particle filter track-before-track (TBD) methods. It is shown that the former outperforms the latter. A conventional detection and tracking (CDT) approach, based on one-data-frame thresholding, is considered as a benchmark for comparison. Simulations demonstrate the performance.