class HKalDafSingleWire: public HKalFiltWire

 Calculates an effective measurement error matrix from the measurement
 errors of all hits in this site.
 Used for the annealing filter.
 Effective measurement error covariance:
 V = [ sum(p_i * G_i) ]^-1
 p_i: weight of hit i
 G_i: inverse measurement error matrix of hit i

 Calculates an effective measurement vector from the measurement
 of all hits in this site and the effective measurement error matrix.
 Used for the annealing filter.

 Effective measurement vector:
 m_eff = V * sum(p_i * G_i * m_i)
 V:   effective measurement covariance matrix
 p_i: weight of hit i
 G_i: inverse measurement error matrix of hit i
 m_i: measurement vector of hit i

 Effective measurement error covariance:
 V = [ sum(p_i * G_i) ]^-1

 Runs the Kalman filter over a set of measurement hits.
 Returns false if the Kalman filter encountered problems during
 track fitting or the chi^2 is too high.

 Input:
 hits:        the array with the measurement hits. Elements must be of
              class HKalMdcHit.
 iniStateVec: initial track state that the filter will use.
 iniCovMat:   initial covariance matrix that the filter will use.
 pId:         Geant particle id.

 Set parameters for the deterministic annealing filter.

 input:
 chi2cut: Cut-off parameter.
 T:       Array with annealing factors (temperatures).
 n:       Number of annealing factors.

 Returns the effective measurement error of site.

 Input:
 site: measurement site.

 Returns the effective measurement vector of measurement site.

 Input:
 site: measurement site.

 Returns number degrees of freedom, i.e. number of experimental points
 minus number of parameters that are estimated.

 Replace sites of the Kalman system with new hits.
 hits: array with new measurement hits. Must be of class HKalMdcHit.