Infrared　small　target　detection　is　critical　to　infrared　search　and　tracking　(IRST)　systems.　However,　accurate　and　robust　detection　remains　challenging　due　to　the　scarcity　of　target　information　and　the　complexity　of　clutter　interference.　Existing　methods　have　some　limitations　in　feature　representation,　leading　to　poor　detection　performance　in　complex　scenes.　Especially　when　there　are　sharp　edges　near　the　target　or　in　cluster　multi-target　detection,　the　“target　suppression”　phenomenon　tends　to　occur.　To　address　this　issue,　we　propose　a　robust　unsupervised　multi-feature　representation　(RUMFR)　method　for　infrared　small　target　detection.　On　the　one　hand,　robust　unsupervised　spatial　clustering　(RUSC)　is　designed　to　improve　the　accuracy　of　feature　extraction;　on　the　other　hand,　pixel-level　multiple　feature　representation　is　proposed　to　fully　utilize　the　target　detail　information.　Specifically,　we　first　propose　the　center-weighted　interclass　difference　measure　(CWIDM)　with　a　trilayer　design　for　fast　candidate　target　extraction.　Note　that　CWIDM　also　guides　the　parameter　settings　of　RUSC.　Then,　the　RUSC-based　model　is　constructed　to　accurately　extract　target　features　in　complex　scenes.　By　designing　the　parameter　adaptive　strategy　and　iterative　clustering　strategy,　RUSC　can　robustly　segment　cluster　multi-targets　from　complex　backgrounds.　Finally,　RUMFR　that　fuses　pixel-level　contrast,　distribution　and　directional　gradient　features　is　proposed　for　better　target　representation　and　clutter　suppression.　Extensive　experimental　results　show　that　our　method　has　stronger　feature　representation　capability　and　achieves　better　detection　performance　than　several　state-of-the-art　methods.　Authors