This　paper　describes　the　distributed　information　filtering　where　a　set　of　sensor　networks　are　required　to　simultaneously　estimate　input　and　state　of　a　linear　discrete-time　system　from　collaborative　manner.　Our　research　purpose　is　to　develop　a　consensus　strategy　in　which　sensor　nodes　communicate　within　the　network　through　a　sequence　of　Kalman　iterations　and　data　diffusion.　A　novel　recursive　information　filtering　is　proposed　by　integrating　input　estimation　error　into　measurement　data　and　weighted　information　matrices.　On　the　fusing　process,　local　system　state　filtering　transmits　estimation　information　using　the　consensus　averaging　algorithm,　which　penalizes　the　disagreement　in　a　dynamic　manner.　A　simulation　example　is　provided　to　compare　the　performance　of　the　distributed　information　filtering　with　optimal　Gillijins-De　Moor＇s　algorithm.