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Joint domain localisation (JDL) is a popular reduced-dimension space-time adaptive processing (STAP) technique for clutter suppression in an air-borne radar system. Here, the authors develop an improved JDL method by exploiting persymmetric covariance matrix, referred to as persymmetric joint domain localisation (Per-JDL), in order to make maximum use of training samples and further improve the STAP performance under small training data support. The proposed algorithm is verified to be efficient in training-limited scenarios by simulation results.