12.1 Introduction

This chapter presents a deterministic least-squares adaptive signal processing technique for nulling interferers and estimating signals of interest (SoI) in the presence of clutter and transient electromagnetic interference. Unlike stochastic methods which rely upon auxiliary training data to estimate the statistics of the interference, in order to place nulls, this approach operates on a snapshot-by-snapshot basis to determine adaptive weights. Due to the reduced training data set (one range ring only - the range ring under test) and because of the particular least-squares techniques employed, this approach can readily be implemented in real time in field-deployable sensor signal processing systems. From a scientific standpoint, a deterministic method provides an estimate which comes closer to the Cramer-Rao bound than do the results provided by a stochastic methodology [1]. Another advantage of this method over conventional stochastic methods is that it operates effectively in highly non-stationary environments and is effective against fluctuating interferers as well ^[1]. In fact, this may be its greatest strength. The deterministic least-squares adaptive signal processing technique introduced here has application in many fields, including: communications, radar, sonar, medical imaging etc. The application addressed in this chapter is airborne radar, of which a description can be found in the literature [2, 3].

In airborne radar surveillance systems, the detection of air and ground targets is complicated by many factors, as illustrated in Figure 12.1 [3]. In this Figure, the target signal...