For　tracking　in　complicated　environment,　a　nonlinear　target　tracking　method　based　on　optimized　wavelet　features　is　proposed.　Gabor　wavelet　network　(GWN)　is　used　to　describe　the　features　of　the　object.　GWN　includes　a　set　of　wavelets,　and　each　of　their　parameters　is　computed　by　optimization　procedure.　The　tracking　framework　is　based　on　optimized　particle　filter　and　each　particle　figures　a　set　of　possible　motion　parameters.　L-M　optimization　is　then　employed　to　drive　the　particles　to　the　local　peak　values,　and　tracking　with　optimized　particle　filters　is　robust　and　efficient　as　a　result　of　multimodality.　The　tracking　result　shows　that　the　algorithm　is　robust　to　illumination　variation　and　noise,　and　it　also　has　the　strong　ability　of　tracking　under　local　occlusion.　Compared　with　standard　particle　filter　method,　the　average　tracking　error　of　the　proposed　algorithm　is　within　1　pixel,　which　has　been　reduced　by　50%.