Overview of RRM Techniques

Contents

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:...