ELECTROMAGNETIC SIGNAL PROCESSING: A CASE STUDY

Amongst the electromagnetic field computation research areas growing in importance, we may include electromagnetic signal and image processing, electromagnetic sensors for automated systems, passive and active remote sensing and electromagnetic neural networks. In this chapter we summarize some of our work in the area of electromagnetic field-based state estimation, tracking and adaptive control. This subject—the merging of electromagnetic field computation with signal processing techniques—is of vast importance, and holds much promise for aerospace and medical electronics. We propose a system for runway plane based measurements of the approach angle of an aircraft, the relative position of the aircraft plane with respect to the runway plane and the flight path. What we present here is an inverse electromagnetic solver for ultra high frequency (UHF) radar. This chapter presents a review of the sensing and tracking system proposed, a non-optimal formulation of aircraft plane measurement using the scattered electromagnetic fields captured by radar, an optimal Kalman estimator and tracking algorithm for the electromagnetic field based system and a further application of the state estimator and tracking system for adaptive control of a vibrating body using a static electric field. The findings reported in this chapter can be summarized as follows: • The magnitude of the Doppler radar electromagnetic fields scattered by an aircraft, and its phase with respect to the transmitted electromagnetic pulse contain information on the aircraft wing position and its body position with respect to the radar plane. The scattered signal received by the radar is assumed to be corrupted by additive white Gaussian noise (AWGN). A maximum likelihood estimation (MLE) algorithm operating on the scattered electromagnetic field model is used to determine the position of the aircraft plane with respect to the radar reference plane.