A confluence of existing researches have shown that using priori knowledge can efficiently mitigate the heterogeneous clutter of airborne/space-based radar.A brief overview of significant progress in knowledge-aided (KA) methods for airborne radar clutter suppression is presented.The recent advances of clutter mitigation methods exploiting a prior knowledge in indirect way

such as associating radar data with dissimilar data

intelligent training and filter selecting

are introduced.Especially

some methods for registration of radar data and other resources data are elaborated.The progress in Bayesian filtering and data pre-whitening which can directly filter the incoming multi-dimensional data stream are analyzed

including the priori covariance matrix estimation

pre-whitened STAP filtering and Bayesian STAP filtering.Furthermore

a brief overview of the KA CFAR algorithms

high-fidelity clutter modeling and validation data of KA-based airborne radar adaptive processing algorithms are conducted.Although great progress has been made in clutter mitigation methods using KA

there are some unsolved problems in the real application.For these problems

we give some idea about using multiple data sources and effectively exploiting prior knowledge.