Srihari, P.Dewangan, V.K.Anvith, M.Jayan, A.Anurag, M.Pardhasaradhi, B.2026-02-062021Proceedings of the 2021 IEEE 18th India Council International Conference, INDICON 2021, 2021, Vol., , p. -https://doi.org/10.1109/INDICON52576.2021.9691752https://idr.nitk.ac.in/handle/123456789/30215Electromagnetic (EM) absorbers are used in the surveillance to inhibit the target reflections back to the EM sensor. In the presence of EM absorbers, the probability of detection (PD) of a target in a particular area is very low or zero. Hence, the traditional trackers report death (termination of an existing track) whenever the target enters the EM absorber zone. Similarly, the tracker reports as birth (initialization of new track) whenever the target comes out of the EM absorber zone. Hence, the continuous track appears as a track breakage due to the dead zone. This paper proposes an environment learning with previous knowledge of tracks, and accordingly track management rules are adapted. Based on the knowledge, whenever targets enters into dead zone, the tracker propagates the tracks throughout the dead zone based on the kinematics of the target. The proposed algorithm is demonstrated by using the Histogram probabilistic multi-hypothesis tracker (H-PMHT) owing to its performance and computational load. The simulation results reveal that the proposed knowledge aided track management provides a continuity in track in the presence of EM absorber. © 2021 IEEE.EM absorberH-PMHTknowledge-based trackertrack before detecttrack management