To address this issue, this paper proposes an anti-occlusion vi-sual tracking algorithm for UAVs with multi-feature adaptive fusion named multi-feature adaptive fusion and anti-occlusion tracker (MAFAOT). It introduces a novel approach for implementing an adaptive. Multisensor fusion in optoelectronic target tracking integrates data from radar, EO/IR, and lidar sensors using probabilistic methods and Kalman filtering to enhance accuracy in dynamic scenarios. Specifically, a local Poisson multi-Bernoulli mixture (PMBM) filter was first used. Widespread application of unmanned aerial vehicles (UAVs) has brought new military threats. However, the stable tracking, classification and iden-tification of UAV targets in complex environments restricts the overall improve-ment of the scale application and capabilities of anti-UAV systems. In. algorithms to estimate the states of multiple targets in clutter and multisensor information fusion. active and passive sensors are discussed.
