An inertial measurement system comprising: at least one sensor cluster comprising a plurality of inertial sensors (710 1 ,...,710 J ; 712 1 ,...,712 L' ) for sampling at least one of acceleration and angular velocity of said at least one sensor cluster with respect to each axis in a plurality of axes of a reference frame , and for producing individual outputs associated with said at least one of acceleration and angular velocity, at least three of said inertial sensors said sampling with respect to each same respective said axis, and a processing engine configured to receive said individual outputs, and, to dynamically self-calibrate a parameter associated with at least one outputted measurement by: combining said individual outputs of a particular sensor type, per said axis, by weighted averaging to yield respective combined outputs; detecting, which of said individual outputs diverges from their respective combined output, according to a decision rule; estimating revised combined outputs from only those said individual outputs not diverging from their respective said combined output; determining respective re-calibration parameters only for those sensors whose individual sensor outputs were detected as diverging from their respective combined output; and applying the re-calibration parameters to those sensors whose individual sensor outputs were detected to diverge, by respectively applying individual gain corrections, wherein said re-calibration parameters include individual scale factors of those sensors whose said individual outputs were detected to diverge. at least one sensor cluster comprising a plurality of inertial sensors (710 1 ,...,710 J ; 712 1 ,...,712 L' ) for sampling at least one of acceleration and angular velocity of said at least one sensor cluster with respect to each axis in a plurality of axes of a reference frame , and for producing individual outputs associated with said at least one of acceleration and angular velocity, at least three of said inertial sensors said sampling with respect to each same respective said axis, and a processing engine configured to receive said individual outputs, and, to dynamically self-calibrate a parameter associated with at least one outputted measurement by: combining said individual outputs of a particular sensor type, per said axis, by weighted averaging to yield respective combined outputs; detecting, which of said individual outputs diverges from their respective combined output, according to a decision rule; estimating revised combined outputs from only those said individual outputs not diverging from their respective said combined output; determining respective re-calibration parameters only for those sensors whose individual sensor outputs were detected as diverging from their respective combined output; and applying the re-calibration parameters to those sensors whose individual sensor outputs were detected to diverge, by respectively applying individual gain corrections, combining said individual outputs of a particular sensor type, per said axis, by weighted averaging to yield respective combined outputs; detecting, which of said individual outputs diverges from their respective combined output, according to a decision rule; estimating revised combined outputs from only those said individual outputs not diverging from their respective said combined output; determining respective re-calibration parameters only for those sensors whose individual sensor outputs were detected as diverging from their respective combined output; and applying the re-calibration parameters to those sensors whose individual sensor outputs were detected to diverge, by respectively applying individual gain corrections, wherein said re-calibration parameters include individual scale factors of those sensors whose said individual outputs were detected to diverge. <EOS>