In　recent　years,　visual　tracking　has　witnessed　remarkable　advancements　with　the　exploration　of　feature　extraction　and　correlation　modeling　techniques.　However,　inadequate　robustness　of　either　the　backbone　network　or　the　correlation　operation　continues　to　plague　existing　trackers,　leading　to　frustrating　drift　when　confronted　with　similar　distractors　or　cluttered　backgrounds.　To　address　this　problem,　we　propose　a　hierarchical　attention-enhanced　correlation　refinement　network　(HarNet)　for　achieving　robust　visual　tracking.　Specifically,　a　gated　dual-view　attention　(GDA)　module　is　first　designed　to　aggregate　the　intra-layer　attention　and　the　inter-layer　self-attention　based　on　a　fusion　gate,　so　as　to　enhance　hierarchical　feature　representations　of　the　template.　Meanwhile,　a　target-aware　attention　(TA)　module　introduces　the　template　information　to　the　inter-layer　self-attention,　which　can　highlight　the　target　information　in　the　search　region.　Moreover,　a　graph　guided　correlation　(GGC)　module　leverages　the　pixel-to-local　and　pixel-to-global　correlations　to　fully　exploit　both　local-and　global-spatial　information　between　the　template　and　the　search　region,　and　then　uses　the　graph　convolutional　network　(GCN)　to　further　learn　the　node　relationships　of　the　correlation　map　for　more　finegrained　correlations.　Thus,　with　the　above　three　elaborately　designed　modules,　the　HarNet　is　beneficial　for　the　enhancement　of　feature　representation　and　the　precise　localization　of　the　target.　Extensive　experiments　on　popular　visual　tracking　datasets　(including　OTB100,　VOT2016,　VOT2018,　VOT2019,　UAV123,　UAV20L,　GOT-10k,　and　LaSOT)　demonstrate　the　superiority　of　our　proposed　method　against　several　state-of-the-art　tracking　methods.