空间时间自适应雷达处理：前沿技术与实践指南

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"《空间时自适应处理在雷达中的应用》是一本由J.R. Guerci所著的经典国外书籍，它深入探讨了阵列信号处理领域的核心内容，特别是在雷达技术中的空间时间自适应处理（Space-Time Adaptive Processing, STAP）。本书作为Artech House Radar Library系列的一部分，旨在提供最新的研究成果和技术进展，使读者能够了解这一前沿技术在实际雷达系统中的关键作用。 STAP是一种先进的信号处理方法，它通过利用多个天线元素（或阵列）的协同工作，动态地调整其方向性和频率响应，以增强接收信号的质量，同时抑制干扰和噪声。这种技术对于现代雷达系统至关重要，因为它提高了系统的抗干扰能力，从而提升目标检测和跟踪性能，尤其是在复杂的电磁环境下。书中详细介绍了STAP的基本原理、算法设计、实施步骤以及其在实际雷达系统中的应用场景。读者可以了解到如何构建自适应天线阵列，如何利用统计学习方法提取目标和干扰的特征，以及如何通过实时数据处理进行快速决策。此外，书中还涵盖了相关的理论基础，如概率统计、信号模型和优化理论，确保了理论与实践的紧密结合。为了保持与行业发展的同步，作者特别提到了Artech House Radar Library的其他近期出版物，鼓励读者探索更广阔的学术视野。该书不仅适合雷达工程专业的学生和研究人员，也对从事信号处理、电子战和通信领域的工程师具有很高的参考价值。版权信息表明，这本书遵循严格的版权规定，禁止任何形式的复制或机械性利用，旨在保护作者的知识产权并维护高质量的专业内容。《空间时自适应处理在雷达中的应用》是一部权威且实用的教材，对于理解和应用阵列信号处理技术有着不可替代的作用。"

2 Space-Time Adaptive Processing for Radar

Figure 1.1 Illustration of (a) clutter iso-range ring and angle-Doppler dependence for a

ULA radar in constant-velocity motion, and (b) corresponding angle-Doppler

clutter ‘‘ridge’’ arising from main lobe and sidelobe antenna patterns. Note

that a target of interest generally must compete with either sidelobe or main

lobe clutter leakage.

Introduction

However, when ownship motion is present, ground returns are Doppler

shifted according to the relationship [1, 2]

␤␪

(1.1)

where

␪

and f

are the normalized angle and Doppler respectively (see

Chapter 3) and

␤

is a proportionality constant that depends on ownship

speed, pulse repetition frequency (PRF), and array interelement spacing (see

Chapter 3).

As illustrated in Figure 1.1, ownship velocity gives rise to 2-D clutter

‘‘ridges’’ whose slope is governed by (1.1). Moreover, due to the Nyquist

frequency of ±PRF/2 [3], there may be more than one distinct ridge due

to Doppler aliasing [1, 2]. As a consequence, it is likely that for every

target of interest, there is a region of ground clutter with the same Doppler

frequency—but offset in angle—that potentially could mask the target

through either sidelobe or main lobe leakage [1, 2]. The objective of space-

time processing is to place a null in the angle-Doppler beampattern where

clutter may compete. Due to the aforementioned 2-D nature of clutter, this

null will depend on the target angle-Doppler of interest [1, 2].

While (1.1) establishes the basic need for space-time (angle-Doppler)

processing, it in no way implies the need for adaptivity. Since it is conceivable

to know the relative orientation of the radar and ground surface through

ownship inertial navigation systems (INS) and global positioning systems (GPS),

as well as basic ground topology and clutter prediction maps [5], one could

simply steer a deterministic null in the predicted clutter direction [see (1.2)].

Unfortunately, a multitude of practical real-world considerations severely

limits the efficacy of this technique. Indeed, many of these considerations

constitute both the impetus and subject matter of this book. Examples

of such considerations include channel mismatch, clutter heterogeneity/

nonstationarity, ICM, and other interference subspace leakage (ISL) mecha-

nisms (see Chapters 2 through 4). It is these effects that give rise to the

inherent need for adaptivity—hence STAP.

1.2 STAP for MTI Radar

Figure 1.2 shows the basic schematic architecture of a STAP beamformer.

For ease of analysis we will nominally consider an N-element ULA and a

coherent processing interval (CPI) consisting of M pulses with a fixed pulse

repetition interval (PRI) (as notionally depicted in Figure 1.2)—although

6 Space-Time Adaptive Processing for Radar

[1, 2]. Although there are many variations on the theme (many of which

are described in this text), the basic underlying optimum beamformer result

is given by

w = R

−

s (1.3)

where as before, s is the desired signal, and w is the optimal set of combiner

weights. R is the positive-definite NM × NM dimensional covariance matrix

[1, 2] associated with the total interference (clutter plus jamming plus receiver

noise) for the given range cell under test [1, 2]. Since we have already

stipulated that it is not possible to characterize the space-time interference

a priori with sufficient precision to effect adequate nulling, it may at first

appear that (1.3) is a mere tautology. However, under conditions discussed

in Chapters 3 and 4, and elsewhere [9], it may be possible to estimate R

from sample data obtained in the normal course of radar operation. Indeed,

it is the estimation of R on the fly that is the true basis for the inclusion of

the word ‘‘adaptive’’ in STAP [1, 2].

However, as will be made clear in Chapters 2 through 5, great care

may be required in practice to glean the essential information from the

environment since the amount of data available for estimating R is fundamen-

tally limited not only by the resolution properties of the radar, but by the

environment itself [10]. This latter consideration is particularly acute in

highly heterogeneous and nonstationary terrain that can be expected to occur

in a variety of potential real-world operating conditions [5, 10, 11]. It is

the intention of this book to introduce the reader to an important cross

section of these issues and to introduce STAP methods which have the

potential of meeting those challenges.

A clear illustration of the conspicuous differences between real-world

and synthetic, or mathematically idealized, ground clutter is afforded by

examination of the returns from the Defense Advanced Research Projects Agency

(DARPA) mountaintop radar [12], shown in Figure 1.4. Although ground-

based, airborne MTI radar operation was emulated by a so-called inverse

displaced phase center array (IDPCA) technique in which successive pulses

were transmitted from a different phase center along the ULA [12]. Thus,

as far as ground clutter was concerned, the illumination appeared to be

moving.

Figure 1.5 shows the processed returns as a function of range and

Doppler for both the actual radar and a simulation based on a homogenous

clutter assumption [5]. The radar was situated at the White Sands Missile

Range (WSMR). It is clearly not hard to imagine the mismatch pitfalls that

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