The traffic matrix (TM) is one of the crucial inputs in many network management and traffic engineering tasks. As it is usually impossible to directly measure traffic matrices, it becomes an important research topic to infer traffic matrix by reasonably modeling, and incorporating the measurement data of limited links, as well as other additional information. In this paper, we propose Square Root Filtering/Smoothing traffic matrix estimation (SRFsTME) algorithm based on Kalman Smoothing decomposition to improve our proposed Square Root Kalman Filtering traffic matrix estimation (SRKFTME) algorithm. Simulation and actual traffic testing results show that SRFsTME algorithm is more numerical accurate and stable than the SRKFTME algorithm.
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