Reconciliation of process data using other projection matrices

Two other projection matrices, used in the solution of data reconciliation problems, are described in this short note. The first matrix projection int roduced is straightforward to compute, is idempotent and can be easily upda ted when a measurement is deleted or removed from the problem. The second p rojection matrix, while although being more numerically intensive in its co mputation than the first, may prove superior when ill-behaved or ill-condit ioned systems are reconciled given that it employs the very numerically sta ble singular value decomposition. Two small examples, one non-linear and th e other linear, are presented which demonstrate the use of the new projecti on matrices and serve as a comparison to the well-known matrix projection o f Crowe, Garcia, & Hrymak (1983). (C) 1999 Elsevier Science Ltd. All rights reserved.