Overview

It was pointed out in Chapter 5 that linear space-time transforms cna be used to reduce the signal vector space down to the clutter subspace which subspace which leads to a reduction of the computational expense in the processing.

However, we noticed a dependency between the required number of degrees of freedom of the processor and the dimension of the signal vector space (number of antenna elements N, echo sample size M). Therefore, these techniques are useful mainly for small antenna arrays and small echo sample size.

If was found furthermore that such systems may suffer from a lack of degrees of freedom in the case of additional eigenvalues due to bandwidth effects or channel erros.

In this chapter we analyse the effect of spatial transforms in the context of spacetime adaptive MTI filters. Such transforms have been widely used in adaptive jammer nulling. One prominent example is the sidclobe canceller, see Section 1.2.3.1. For some details of spatial transforms see Section 1.2.3.

While the space-time transforms treated in the previous chapter ^[1] reduce the signal vector space in both the spatial and the temporal dimension simultaneously spatial transform reduce the spatial dimension only. There are many ways of designing a spatial order reducing transform. On possible solution is the partner filter approach by WIRTH [552]. In Chapter 7 we will discuss a way of simplifying the adaptive processor in the time dimension. We will concentrate in the sequel on techniques using subarrays or...