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G. Robert Redinbo, "Generalized AlgorithmBased Fault Tolerance: Error Correction via Kalman Estimation," IEEE Transactions on Computers, vol. 47, no. 6, pp. 639655, June, 1998.  
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@article{ 10.1109/12.689644, author = {G. Robert Redinbo}, title = {Generalized AlgorithmBased Fault Tolerance: Error Correction via Kalman Estimation}, journal ={IEEE Transactions on Computers}, volume = {47}, number = {6}, issn = {00189340}, year = {1998}, pages = {639655}, doi = {http://doi.ieeecomputersociety.org/10.1109/12.689644}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Computers TI  Generalized AlgorithmBased Fault Tolerance: Error Correction via Kalman Estimation IS  6 SN  00189340 SP639 EP655 EPD  639655 A1  G. Robert Redinbo, PY  1998 KW  Algorithmbased fault tolerance KW  faulttolerant linear processing KW  Kalman recursive filtering KW  meansquare error estimation KW  real convolutional codes KW  real number error correction KW  timevarying fault models KW  totally selfchecking comparators. VL  47 JA  IEEE Transactions on Computers ER   
Abstract—An extension to AlgorithmBased Fault Tolerance (ABFT) methodologies shows how parity values dictated by a real convolutional code can be employed by Kalman estimation techniques to perform real number correction for protecting linear processing systems. Intermittent failures appearing in the output samples are detected and corrected using only the syndromes normally generated in ABFT schemes. The algebraic structure of a real convolutional code provides separation needed by recursive Kalman state estimators to affect meansquare error correction. State and parity measurement equations model faults and computational noise in both the linear processing and parity generation subassemblies, and, in a departure from previous models, the noise sources are considered timevarying. The Kalman onestep estimator which makes decisions on all parity values up to the present point is determined, and it separates naturally into detection and correction operations permitting corrective action only when the detection levels exceed thresholds based on roundoff noise energy. The detector/corrector uses efficient multirate block processing techniques as determined by the real convolutional code.
A smoothed fixedlag Kalman estimator which uses parity values for a fixed amount beyond the point of interest is needed to complete the correction. It employs onestep estimator quantities and implementation simplifications are possible. Examples showing the correction behavior and meansquare error performance are presented, and the size of overhead calculations for detection and correction is estimated. A protected processing system is constructed by introducing additional subassemblies, mostly comparators, with the detection and correction parts already described. Under the usual assumptions of at most a single subassembly failure, no improperly detected or corrected data leave the overall protected configuration.
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