Under　indoor　environments,　positioning　and　tracking　using　GPS　and　radar　measurements　are　very　scarce.　Comparatively,　positioning　and　tracking　using　received　signal　strength　(RSS)　measurements　from　wireless　sensor　networks　are　gaining　more　and　more　attention.　However,　so　far　all　localization　or　tracking　algorithms　did　not　take　systematic　sensor　biases　into　account.　If　the　biases　are　not　corrected,　they　will　lead　to　degradation　in　tracking　performance.　In　this　paper,　we　propose　a　framework　to　jointly　estimate　the　dynamic　source　state　and　static　sensor　biases　using　nonlinear　filters　such　as　Extended　Kalman　filter　(EKF)　and　Unscented　Kalman　Filter　(UKF).　Numerical　examples　show　that　this　framework　can　estimate　both　source　state　and　sensor　biases　very　well.