Simplified parameter quantization procedure for adaptive estimation

Abstract

Optimum Kalman filter design often requires estimation of the true value of an unknown parameter vector. In Magill's adaptive procedure, the parameter space must be quantized. An accurate estimate of the true value requires fine quantization, but this results in an unreasonable number of elemental filters. Iterative techniques that require only binary quantization of each unknown parameter are proposed. This reduces the number of elemental filters without sacrificing accuracy of the parameter estimate.