Radar-based　Human　Pose　Estimation　(R-HPE)　aims　to　locate　the　body　joints　of　each　individual　in　a　given　radar　image.　This　is　relevant　for　various　applications　such　as　action　recognition,　person　re-identification,　and　human-object　interaction.　Unlike　traditional　RGB-based　human　pose　estimation,　radar-based　human　pose　estimation　can　effectively　preserve　human　privacy　and　remain　stable　under　low-light　conditions　and　darkness.　However,　research　on　radar-based　human　pose　estimation　is　limited,　and　existing　methods　fail　to　adequately　model　radar　features,　resulting　in　lower　accuracy　of　corresponding　pose　estimation　algorithms.　Therefore,　this　paper　proposes　a　dual-branch　structured　network,　which　can　extract　dimension-independent　features　and　dimension-dependent　features　separately　and　then　combine　them　for　more　precise　decision-making.　This　allows　the　network　to　learn　richer　and　more　diverse　feature　representations,　thereby　improving　the　quality　of　feature　extraction.　Meanwhile,　a　Multi-Dimensional　Feature　Fusion　Network　extracts　more　detailed　feature　representations.　Furthermore,　it　is　combined　with　a　Transformer　module　to　further　enhance　the　model＇s　ability　to　extract　local　features　and　global　modeling　capabilities,　thereby　improving　the　accuracy　of　human　pose　estimation.　Extensive　experiments　conducted　on　the　HuPR[10]　dataset　demonstrate　that　our　model　outperforms　existing　state-of-the-art　models　in　terms　of　human　pose　estimation　performance.　©　2024　IEEE.