A multi-frame track-before-detect algorithm based on root label clustering for multiple targets

In this paper, a novel multi-frame track-before-detect algorithm is proposed, which is based on root label clustering to reduce the high computational complexity arising by observation area expansion and clutter/noise density increase. A criterion of track extrapolation is used to construct state transition set, root label is marked by state transition set to obtain the distribution information of multiple targets in measurement space, then measurement plots of multi-frame are divided into several clusters, and finally multi-frame track-before-detect algorithm is implemented in each cluster. The computational complexity can be reduced by employing the proposed algorithm. Simulation results show that the proposed algorithm can accurately detect multiple targets in close proximity and reduce the number of false tracks.