Model-Based Processing

1Introduction

In this chapter, we introduce the idea of model‐based identification, starting with the basic notions of signal processing and estimation. Once defined, we introduce the concepts of model‐based signal processing, that lead to the development and application of subspace identification. Next, we show that the essential ingredient of the model‐based processor is the “model” that must be available either through the underlying science (first principles) or through the core of this text – model‐based identification.

1.1 Background

The development of processors capable of extracting information from noisy sensor measurement data is essential in a wide variety of applications, whether it be locating a hostile target using radar or sonar systems or locating a tumor in breast tissue or even locating a seismic source in the case of an earthquake. The nondestructive evaluation (NDE) of a wing or hull of a ship provides a challenging medium even in the simplest of arrangements requiring sophisticated processing especially if the medium is heterogeneous. Designing a controller for a smart car or a drone or for that matter a delicate robotic surgical instrument also depends on providing enhanced signals for feedback and error corrections. Robots replacing humans in assembly lines or providing assistance in mundane tasks must sense their surroundings to function in a such a noisy environment. Most “hi‐tech” applications require the incorporation of “smart” processors capable ...