Abstract
AbstractTo address the interference caused by multipath ghosts, this paper presents a multipath target discrimination method based on separated Gaussian similarity matrices. The proposed method utilizes separated Gaussian similarity functions based on the Maximum Likelihood principle to quantify the similarity from distance and angle perspectives. Meanwhile, dimensionality reduction is applied to maximize the distinction between multipath ghosts and actual targets. This method can effectively discriminate the multipath relationships and attributes of detected targets and has higher recognition accuracy than other geometry‐based methods. The effectiveness and superiority of the proposed method were validated through Monte‐Carlo and practical experiments.
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