In　order　to　realize　the　recognition　of　human　posture　in　complex　and　diverse　environments,　a　method　based　on　Frequency　Modulated　Continuous　Wave　(FMCW)　radar　signal　is　proposed.　This　method　obtains　multi-dimensional　information　of　distance,　speed　and　angle　by　performing　3D　fast　Fourier　transform　on　the　original　signal　of　FMCW　radar.　After　using　the　Density-Based　Spatial　Clustering　of　Applications　with　Noise　(DBSCAN)　algorithm　and　the　Hampel　filter　algorithm　to　solve　the　noise　interference　of　dynamic　or　static　targets　in　the　range　of　motion,　the　convolutional　neural　network　is　used　to　extract　the　features　of　multidimensional　information.　Then　Low-rank　Multimodal　Fusion　network　(LMF)　is　used　to　fully　fuse　the　features　of　multi-dimensional　information.　Finally,　the　domain　discriminator　further　obtains　domain-independent　features,　and　the　activity　recognizer　obtains　the　result　of　gesture　recognition.　The　pre-designed　algorithm　and　the　trained　network　model　are　carried　out　on　the　edge　computing　platform　for　experimental　verification.　Experimental　results　show　that　the　recognition　accuracy　of　this　method　can　reach　91.5%　in　complex　environments.　©　2022　Science　Press.　All　rights　reserved.