Papers by Keyword: Track-Before-Detect

Abstract: This paper deals with the problem of time-varying multitarget track-before-detect (TBD) using image observation model. The multitarget state is formulated as random finite set (RFS) and its posterior distribution is approximated by multi-Bernoulli parameters, which are recursively evaluated using sequential Monte Carlo approach. The state estimates are first extracted from the updated Bernoulli components with moderate existence probabilities, allowing for all the true targets and false alarms. The extracted target states are then distilled using track consistency test strategy to remain only the true tracks. Simulation results show the improved performance of the proposed method over the traditional multitarget multi-Bernoulli (MeMBer) filter as well as its capability to provide the identity of individual target.

Abstract: This paper presents a track-before-detect (TBD) algorithm for detection of unknown quantity of targets with parabolic tracks. First, eliminate large number of clutters orderly by expanded trellis and the method of mean power. And then get candidate parabolas by Randomized Hough Transform (RHT). The true tracks of the targets are extracted successfully by a strategy of outliers eliminating at last. Experimental results indicate that the proposed algorithm is capable of detecting multiple targets without the assumptions of an upper bound to the number of targets.