Introduction

Part III Adaptive Receivers discusses implementation of the third of three

sequentially complementary approaches for increasing the probability of

detection, within at least some cells of the surveillance volume, in presence of

external “noise” which can be either Gaussian or nonGaussian in the spatial

domain but Gaussian in the temporal domain. For each homogeneous subdivision

of the surveillance volume, this approach, identified in the Preface as Approach C,

generally utilizes a nonlinear, nonGaussian receiver whose detection algorithm

is matched to the sampled “noise” voltage spatial probability density function

(PDF) of that subdivision. When the nonGaussian “noise” waveform is spikier

than Gaussian noise, the nonlinear receiver is more effective than a linear receiver

in reducing the detection threshold for a given false alarm rate, provided that the

estimated spatial PDF is an accurate representation of the actual PDF. If the

estimated spatial PDF is Gaussian, then the nonlinear receiver reduces to a linear

Gaussian receiver. The issues are (1) how to model, simulate, and identify the

random processes associated with the correlated “noise” data samples and (2)

how to determine the nonlinear receiver and its threshold which are best matched

to those data samples. Part III Adaptive Receivers addresses these issues and

gives several applications.