Introduction

Part II Adaptive Antennas discusses implementation of the second of three

sequentially complementary approaches for increasing the probability of

detection within at least some cells of the surveillance volume for external

“noise” which can be either Gaussian or non-Gaussian in the spatial domain but is

Gaussian in the temporal domain. This approach, identified in the preface as

Approach B and also known as space-time adaptive processing, seeks to reduce

the competing electromagnetic environment by placing nulls in its principal

angle-of-arrival and Doppler frequency (space-time) dimensions. This approach

utilizes, k ¼ NM samples of signals from N subarrays of the antenna, over a

coherent processing interval containing M pulses to (1) estimate in the space-

time domain, an NM £ NM “noise” covariance matrix of the subarray signals,

(2) solve the matrix for up to N unknown “noise” angles of arrival and M

unknown “noise” Doppler frequencies, and (3) determine appropriate weighting

functions for each subarray which will place nulls in the estimated angle-of-

arrival and Doppler frequency domains of the “noise”. Approach B is a form of

filtering in those domains. Consequently, the receiver detector threshold can be

reduced because the average “noise” voltage variance of the surveillance volume

is reduced. The locations and depths of the nulls are determined by the relative

locations, strengths of the “noise” sources in the space-time domain and by the

differences between the actual and estimated “noise” covariance matrices. The

results are influenced by the finite number k of stochastic data samples and the

computational efficiency in space-time processing of the samples. Part II

Adaptive Antennas addresses these issues and presents physical models for

several applications.