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Benefits of multi-weight optimization in OBE algorithms

Optimal bounding ellipsoid identification algorithms feature a unique data selection process which recursively checks observations for innovation, then assigns weights in accordance with information content. Multi-weight optimization enhances this process by reoptimizing over a number of past weights. This technique offers faster unbiased convergence of the central estimator, improved tracking, increased selectivity, and can be used to decrease computational complexity. These properties potentially decrease idle time in multi-processor systems.