Moo / Ding Adaptive Radar Resource Management

1. Auflage 2015
ISBN: 978-0-12-804210-6
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 158 Seiten

Radar Resource Management (RRM) is vital for optimizing the performance of modern phased array radars, which are the primary sensor for aircraft, ships, and land platforms. Adaptive Radar Resource Management gives an introduction to radar resource management (RRM), presenting a clear overview of different approaches and techniques, making it very suitable for radar practitioners and researchers in industry and universities. Coverage includes: - RRM's role in optimizing the performance of modern phased array radars - The advantages of adaptivity in implementing RRM - The role that modelling and simulation plays in evaluating RRM performance - Description of the simulation tool Adapt_MFR - Detailed descriptions and performance results for specific adaptive RRM techniques - The only book fully dedicated to adaptive RRM - A comprehensive treatment of phased array radars and RRM, including task prioritization, radar scheduling, and adaptive track update rates - Provides detailed knowledge of specific RRM techniques and their performance

Peter W. Moo received a B.Sc. in mathematics and engineering from Queen's University at Kingston and a M.S.E and a Ph.D. in electrical engineering: systems from the University of Michigan, where he was a National Science Foundation Graduate Fellow. In 1997 he was a visiting researcher at General Electric Corporate Research and Development, Schenectady, NY. From 1998 to 1999 he was a postdoctoral fellow in the Department of Computer Science at the University of Western Ontario. Since 1999, he has been a defence scientist at Defence R&D Canada, where he is currently Leader of the Wide Area Surveillance Radar Group in the Radar Sensing & Exploitation Section. His research interests include MIMO radar, space-time adaptive processing, and radar resource management.

Autoren/Hrsg.

Moo, Peter

Ding, Zhen

Leseproben

Chapter 2 Overview of RRM Techniques
Abstract
This chapter provides a preliminary survey of the multifunction phased array radar resource management algorithms. The survey summarizes important papers to illustrate existing algorithms for the radar resource optimization problem. The algorithms are categorized into six categories, where the first three categories are adaptive scheduling algorithms and the remaining categories are resource-aided algorithms. The resource-aided algorithms are relevant since a better algorithm needs fewer resources to achieve the same performance. Keywords Multi-function phased array radar Radar resource management Survey Contents 2.1 INTRODUCTION   10 2.2 ARTIFICIAL INTELLIGENCE ALGORITHMS   11 2.2.1 Neural Networks   11 2.2.1.1 Task Prioritization   11 2.2.1.2 Task Scheduling   14 2.2.1.3 Comments   14 2.2.2 Expert System   14 2.2.2.1 Description of the Expert System Approach   14 2.2.2.2 Comments   15 2.2.3 Fuzzy Logic   15 2.2.3.1 Fuzzy Logic Approach   15 2.2.3.2 Comments   17 2.2.4 Entropy   17 2.2.4.1 Entropy Algorithm   17 2.2.4.2 Comments   19 2.3 DYNAMIC PROGRAMMING ALGORITHMS   19 2.3.1 An Example   19 2.3.2 Computational Challenge   20 2.3.3 Some Dynamic Programming Algorithms   20 2.3.4 Comments   21 2.4 Q-RAM ALGORITHMS   22 2.4.1 Introduction   22 2.4.2 Mathematical Formulation   22 2.4.3 Some Q-RAM Algorithms   23 2.4.3.1 A Framework of Q-RAM   23 2.4.3.2 Some Q-RAM Algorithms Based on the Resource Management Framework   24 2.4.3.3 Other Q-RAM Algorithms   25 2.4.3.4 Comments   25 2.5 WAVEFORM-AIDED ALGORITHMS   26 2.5.1 Introduction   26 2.5.2 A Neural Network Algorithm   26 2.5.3 The Waveform Selective PDA Algorithm   26 2.5.4 Other Waveform-Aided Algorithms   27 2.5.5 A Literature Survey of Adaptive Radar   28 2.5.6 A DARPA Research Program on Adaptive Waveform Design for Naval Applications   28 2.5.7 Comments   29 2.6 ADAPTIVE UPDATE RATE ALGORITHMS   29 2.6.1 Introduction   29 2.6.2 A Foundation for Adaptive Update Rate Tracking   29 2.6.3 Adaptive Update Rate IMM-MHT Algorithm   30 2.6.4 Other Adaptive Update Rate Algorithms   30 2.6.5 Comments   31 2.7 THE NRL BENCHMARK PROBLEMS AND SOLUTIONS   32 2.7.1 The NRL Benchmark Problems   32 2.7.2 Solutions to the Benchmark Problems   33 2.7.2.1 Solutions to Benchmark 1   34 2.7.2.2 Solutions to Benchmark 2   35 2.7.2.3 A Solution to Benchmark 3   35 2.7.3 Comments   36 2.8 SUMMARY   36 2.1 Introduction
The radar resource management (RRM) algorithms surveyed in this chapter are divided into five categories, with one section devoted to each category. The first three categories are adaptive scheduling algorithms, and the remaining two categories are resource-aided algorithms. When a paper falls into more than one category, it is placed into the most suitable category. Categories 4 and 5 are relevant since a better algorithm is able to achieve the same performance with fewer resources or to achieve a better performance with the same radar resources. Comments are provided for the RRM algorithms in each category. The five categories are: 1. artificial intelligence (AI) algorithms (Section 2.2); 2. dynamic programming (DP) algorithms (Section 2.3); 3. Q-RAM algorithms (Section 2.4); 4. waveform-aided algorithms (Section 2.5); and 5. adaptive update rate algorithms (Section 2.6). In Section 2.7, the Naval Research Laboratory (NRL) benchmark problems are defined and solutions proposed to date are reviewed. Finally, a summary is presented in Section 2.8. 2.2 Artificial Intelligence Algorithms
In this category, 15 papers are noted [7–21]. The papers cover neural network approaches [7–9], expert system approaches [10, 11], and fuzzy logic approaches [12–18]. An entropy approach for radar scheduling is also discussed [21]. Paper [22] belongs to both the AI category and the waveform-aided algorithm category. It will be discussed in the waveform algorithm category in Section 2.5. 2.2.1 Neural Networks
Neural networks (NNs) are used for key elements of RRM: using classification NNs for task prioritization and optimizing NNs for task scheduling. 2.2.1.1 Task Prioritization Classification NN algorithms are primarily used for assignment of priorities to tasks. The input is all required radar tasks, and the constraints are radar time and energy budgets. Optimization could be the minimization of radar resources, given the search, track and engagement performance requirements, or the maximization of the performance by using the available radar resources. Komorniczak [7, 8] proposed an NN priority assignment algorithm. In this algorithm, a feature vector was the input to multi-layer neurons. A training data set was used to adjust weights of the NN. In the application phase, the trained NN generated the priorities based on all given targets feature data. The arbitrary nonlinear mapping capability of the NNs was utilized. The mapping provides target prioritization values, which classify radar targets into different levels. This is necessary when a lot of targets are competing for radar resources. Accordingly, radar resources are first given to those targets with higher priority. For example, the following target features can be used:...