This　paper　is　concerned　with　performing　the　measurement-to-measurement　association　and　bias　estimation　jointly　in　the　presence　of　missed　detections.　An　integrated　mix　integer　programming　(MINLP)　model　is　established　to　determine　the　correspondence　between　local　measurements　and　estimate　sensor　biases　simultaneously.　An　alternation　optimization　mechanism　is　employed　to　solve　the　complicated　MINLP　model.　A　recursive　version　for　bias　estimation　is　developed　that　provides　an　access　to　deal　with　the　measurement　data　sequentially.　Monte　Carlo　simulation　results　are　presented　to　illustrate　our　findings,　as　also　demonstrating　the　effectiveness　of　the　proposed　approach.