With　the　rise　of　embedded　platforms,　offline　application　of　visual　tracking　technology　directly　on　terminal　platforms　will　become　a　popular　remote　sensing　technology　in　the　future.　The　current　development　directions　usually　focus　on　improving　accuracy.　Without　the　GPU　platform,　the　real-time　performance　of　the　algorithm　will　significantly　degrade.　We　propose　a　discriminative　tracking　method　based　on　a　multi-feature　fusion　mechanism,　following　the　basic　framework　of　correlation　filter　trackers.　The　innovation　lies　in　two　aspects:　Firstly,　unlike　conventional　direct　feature　fusion,　we　design　a　feature　weighting　mechanism　to　fuse　two　independently　trained　features,　namely　the　FHOG　feature　and　the　color　histogram　feature.　Experimental　results　demonstrate　that　this　approach　is　more　effective　in　exploiting　the　complementarity　between　features.　Secondly,　to　reduce　unnecessary　computations　and　improve　the　inference　speed　of　the　tracker,　we　design　a　peak　discrimination　strategy　based　on　average　peak　correlation　energy　to　manage　the　scale　adaptation　mechanism　and　feature　model　updates.　Considering　our　tracker　needs　to　track　targets　over　long　distances　in　the　air,　all　experiments　are　conducted　using　the　UAV123　dataset　as　the　test　set.　The　experimental　results　demonstrate　that　the　proposed　model　can　be　deployed　on　small　embedded　systems　without　neural　network　inference　capabilities,　ensuring　both　tracking　accuracy　and　real-time　performance.　©　2024　IEEE.