Preface
This text encompasses the basic idea of the model‐based approach to signal processing by incorporating the often overlooked, but necessary, requirement of obtaining a model initially in order to perform the processing in the first place. Here we are focused on presenting the development of models for the design of model‐based signal processors (MBSP) using subspace identification techniques to achieve a model‐based identification (MBID) as well as incorporating validation and statistical analysis methods to evaluate their overall performance 1. It presents a different approach that incorporates the solution to the system identification problem as the integral part of the model‐based signal processor (Kalman filter) that can be applied to a large number of applications, but with little success unless a reliable model is available or can be adapted to a changing environment 2. Here, using subspace approaches, it is possible to identify the model very rapidly and incorporate it into a variety of processing problems such as state estimation, tracking, detection, classification, controls and communications to mention a few 3,4. Models for the processor evolve in a variety of ways, either from first principles accompanied by estimating its inherent uncertain parameters as in parametrically adaptive schemes 5 or by extracting constrained model sets employing direct optimization methodologies 6, or by simply fitting a black‐box structure to noisy data 7,8. Once the model is extracted ...