Signal Processing for Multistatic Radar Systems

Adaptive Waveform Selection, Optimal Geometries and Pseudolinear Tracking Algorithms

Ngoc Hung Nguyen, Kutluyil Doğançay

• Publisher: Academic Press
• Year: 2019
• Print ISBN: 9780128153147
• eBook ISBN: 9780081026472
• Topic: Technology & Engineering
• Subtopic: Signals & Signal Processing
• Index: Technology & Engineering

Signal Processing for Multistatic Radar Systems: Adaptive Waveform Selection, Optimal Geometries and Pseudolinear Tracking Algorithms addresses three important aspects of signal processing for multistatic radar systems, including adaptive waveform selection, optimal geometries and pseudolinear tracking algorithms. A key theme of the book is performance optimization for multistatic target tracking and localization via waveform adaptation, geometry optimization and tracking algorithm design. Chapters contain detailed mathematical derivations and algorithmic development that are accompanied by simulation examples and associated MATLAB codes. This book is an ideal resource for university researchers and industry engineers in radar, radar signal processing and communications engineering.- Develops waveform selection algorithms in a multistatic radar setting to optimize target tracking performance- Assesses the optimality of a given target-sensor geometry and designs optimal geometries for target localization using mobile sensors- Gives an understanding of low-complexity and high-performance pseudolinear estimation algorithms for target localization and tracking in multistatic radar systems- Contains the MATLAB codes for the examples used in the book

Chapter 1

Introduction

Abstract

Over its history of more than a century, radar has played a fundamental and prominent role in a wide range of civilian and military applications. Multistatic radar is a particular type of radar that incorporates multiple spatially distributed transmitters and receivers. The spatial diversity provided by the separation of transmitters and receivers not only offers several advantages over conventional monostatic radar, but also gives system designers extra degrees of freedom to optimize the radar system design for specific applications. Inspired by the unique peculiarities of multistatic radar in relation to nonlinear signal processing, waveform and geometry, this book presents modern signal processing techniques for multistatic tracking radar systems with the pivotal theme of performance optimization via waveform adaptation, geometry optimization and pseudolinear tracking algorithms. The core of the book is structured into three parts: Part 1 – Adaptive waveform selection, Part 2 – Optimal geometry analysis, and Part 3 – Pseudolinear tracking algorithms. This chapter provides background information and a brief review of radar history, explains the purpose and scope of the book, and outlines the organization of the book.

Keywords

signal processing; radar; radar history; radar applications; monostatic radar; bistatic radar; multistatic radar; target tracking; target localization; target motion analysis; Kalman filter; ambiguity function; waveform diversity; adaptive waveform selection; radar-target geometry; optimal geometry; optimal sensor placement; trajectory optimization; pseudolinear estimation; instrumental variables; bias compensation; closed-form solution; Cramér–Rao lower bounds; Fisher information matrix

Chapter Outline

• 1.1 Historical background
• 1.2 Purpose and scope
• 1.3 Outline of book

1.1 Historical background

Over its history of more than a century, radar has played a fundamental and prominent role in a wide range of civilian and military applications thanks to its capability to operate in all weather, day and night, and to detect, track, and image targets with high accuracy at long stand-off ranges. Nowadays, the use of radar can be found in weather forecasting, remote sensing and mapping, astronomy, aerial and terrestrial traffic control, air defense and weapon control, automotive sensor systems, high-resolution target imaging and recognition, airborne collision avoidance systems, and eldercare and assisted living, to name but a few [1–12].

Radar is essentially an electromagnetic sensor for detecting and ranging objects by radiating electromagnetic wave to illuminate the scene of interest and receiving reflected echoes from the objects. By processing the received signal, radar can make a decision on whether one or more targets are present in the scene of interest, and determine the position and velocity and possibly the size, shape and features of the detected targets. In terms of system geometry, radar can be categorized into three different types: monostatic radar, bistatic radar and multistatic radar. Monostatic radar has a transmitter and a receiver which are collocated at the same site. In contrast, bistatic radar is a radar that operates with a transmitter and a receiver located separately at different sites. Multistatic radar is a generalization of bistatic radar by incorporating multiple transmitters and receivers at different locations while having overlapping spatial coverage, as illustrated in Fig. 1.1.

Figure 1.1 An illustration of a multistatic radar system.

By conventional definition, multistatic radar can be viewed as a system of several individual transmitter–receiver pairs that operate independently [13,14]. Detection, target estimation, and other high-level target information from these transmitter–receiver pairs are communicated to a central processor, where they are combined to improve detection and estimation performance. This type of data fusion is often considered as non-coherent processing. For example, multistatic radar can use multilateration to estimate the target kinematic state (i.e., position and velocity) based on range, Doppler and/or angle measurements obtained independently at different transmitter–receiver pairs within the system. MIMO (multiple-input multiple-output) radar with widely separated antennas is another type of multistatic radar with a different design that distinguishes it from multistatic radar by joint processing at the signal level for both transmission and reception, as well as its close connection to MIMO communications [15,16]. However, this type of multistatic radar is not covered in this book.

The spatial diversity provided by the separation of transmitters and receivers not only brings a number of advantages to bistatic and multistatic radars over monostatic radar, but also gives system designers extra degrees of freedom to optimize the radar system design for specific applications [13,14]. Since the receivers are not collocated with the transmitters, bistatic and multistatic radars are able to counter retrodirective jammers which sense the radar signals from the transmitters and direct jamming signals back towards the transmitters. The bistatic/multistatic geometry also enhances the radar cross section for stealthy targets, thus improving the detection performance. Moreover, the target kinematic state can be estimated with a high accuracy by multistatic radar thanks to the exploitation of multilateration from measurements collected by multiple transmitter–receiver pairs, each having a different bistatic geometry with respect to the target. In addition, multistatic radar can avoid the unfavorable geometry of bistatic radar in which the target is located near the line-of-sight between the transmitter and receiver.

Bistatic and multistatic radars can incorporate their own dedicated transmitters or exploit illuminators of opportunity from other transmission sources [13,14,17]. The illuminators of opportunity may come from existing radar systems (either cooperative or non-cooperative) or from commercial non-cooperative broadcast and communications signals. The radar systems that utilize illuminators of opportunity are commonly known as passive. In a passive configuration, bistatic and multistatic radar can operate in civilian areas, particularly in large cities with multiple airports, where radar transmission may not be allowed. In addition, exploiting the illuminators of opportunity from other existing transmission sources eliminates the construction and operation costs for transmitters and associated equipment.

These advantages of bistatic and multistatic radar come at the expense of increased complexity for system hardware and signal processing, particularly in terms of direct-path signal cancellation and transmitter–receiver synchronization. However, great progress has been made towards solving these complexity issues thanks to the tremendous amount of research dedicated to the topics of beamforming, integrated circuit design, and the Global Positioning System, thus making bistatic and multistatic radar systems perfectly feasible in real-world situations [13].

Bistatic and multistatic radars have experienced periodic resurgences throughout the history of radar. The very first radars were of the bistatic type. Prior to and during World War II, several continuous-wave bistatic radars were developed and deployed in many countries including the United States, United Kingdom, Soviet Union, France, Germany, Japan and Italy. Since the invention of radar duplexer, which allows the use of pulsed waveforms with a single common antenna for both transmission and reception, monostatic radar has dominated the field of radar research mainly due to the advantages of single-site operation. Consequently, all bistatic radar work was discontinued by the end of World War II. The first resurgence of bistatic and multistatic radar started in the 1950s with applications in air defense and ballistic missile launch warning, tactical semiactive homing missiles, and test range instrumentation and satellite tracking. The second resurgence happened in the 1970s and 1980s. A number of experimental bistatic radar systems were tested (but not deployed) in response to the retro-jamming and antiradiation missile threats. The Multistatic Measurement System for ballistic missile tracking, which is a multistatic radar hitchhiker, was deployed in the 1980s. The first experiment of using broadcast transmitters as illuminators of opportunity was conducted during this resurgence period. Since the beginning of the third resurgence in the mid-1990s, extensive research studies have been devoted to bistatic and multistatic radars including image focusing and motion compensation techniques for bistatic Synthetic Aperture Radar, adaptive clutter cancellation methods for bistatic moving target indication, and exploitation of commercial broadcast transmitters for passive radar. A comprehensive review of the history of radar, including bistatic and multistatic radars, is available in [13,14,17].

1.2 Purpose and scope

The ambiguity function is an important and indispensable tool for radar performance analysis [8,18,19]. The ambiguity function essentially characterizes the auto-correlation of the complex envelope of the radar waveform with its shifted copy in time and frequency. The point target response of the waveform is expressed by the ambiguity function as a two-dimensional function of time delay and Doppler shift (or equivalently target range and radial velocity). Based on the shape of the ambiguity function, radar performance can be evaluated in terms of estimation accuracy, target resolution, and clutter cancellation. In monostatic radar, where time delay and Doppler shift are proportional to target range and radial velocity, respectively, the ambiguity function is only dependent on the radar waveform without having any influences from the radar-target geometry. In terms of time delay and Doppler shift, the ambiguity function for bistatic and multistatic radars remains identical to that of monostatic radar. However, this definition of ambiguity function provides relatively little information about radar performance because time delay and Doppler shift have nonlinear relationships with target position and velocity parameters [20,21]. Depending on the application, different coordinate systems can be used to characterize target position and velocity, for example, the North-referenced system, the Cartesian system, and spherical coordinates. Therefore, for bistatic and multistatic radars, the ambiguity functions are often expressed in terms of target position and velocity in these coordinate systems. In such forms, the bistatic and multistatic ambiguity functions are not only dependent on the radar waveform, but also strongly influenced by the radar-target geometry [20,21]. This highlights the impo...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• About the Authors
• Preface
• List of Abbreviations and Symbols
• Chapter 1: Introduction
• Part 1: Adaptive waveform selection
• Introduction
• Chapter 2: Waveform selection for multistatic tracking of a maneuvering target
• Chapter 3: Waveform selection for multistatic target tracking in clutter
• Chapter 4: Waveform selection for multistatic target tracking with Cartesian estimates
• Chapter 5: Waveform selection for distributed multistatic target tracking
• Part 2: Optimal geometry analysis
• Introduction
• Chapter 6: Optimal geometries for multistatic target localization with one transmitter and multiple receivers
• Chapter 7: Optimal geometries for multistatic target localization by independent bistatic channels
• Part 3: Pseudolinear tracking algorithms
• Introduction
• Chapter 8: Batch track estimators for multistatic target motion analysis
• Chapter 9: Closed-form solutions for multistatic target localization with time-difference-of-arrival measurements
• Bibliography
• Index