Reliable Task Planning of Networked Devices as a Multi-Objective Problem Using NSGA-II and Reinforcement Learning

Dongcheng Li,Man Zhao,Zhiming Wu,Qiang Hou

doi:10.1109/access.2022.3141912

Abstract

Tracking and detecting multiple objects is difficult for a single radar device, as it may not have the capacities such as anti-interference and anti-stealth. However, if radar devices of diverse capabilities can be combined to realize collaborative networked operation, the reliability and performance of a radar system in a complex environment can be significantly improved. This paper classify the networked radar-based multi-objective task planning as a combinatorial optimization problem with constraints and abstract a distributed multi-agent system (MAS) model from a networked radar system. A node-selection algorithm was designed based on a greedy policy to narrow down solution space for subsequent networked radar task planning, reduce the amount of calculation, and improve the efficiency of the proposed algorithm. Moreover, focusing on NSGA-II, the proposed algorithm was modified using self-adaptive operators and reinforcement learning. A dual-population strategy was introduced to allow exchanges of multiple individuals between populations during migration, and the number of individuals for the exchange was obtained through reinforcement learning. In this paper, five algorithms are compared and analyzed. In addition, statistical analyses are conducted from four perspectives: the average evaluation value of energy consumption and bandwidth in the Pareto front solutions, the time consumption of the algorithm, and the diversity of the population. The results indicate that among those algorithms, the reinforcement-learning-based RNSGA-II algorithm can produce the best outcomes for networked radar task planning.

Highlights

Target tracking and detection by a single radar is challenging [1], [2]
This paper focuses on networked radar task planning— giving full considerations to various constraints—modeling, and algorithm design for networked radar task planning
The paper focused on networked radar task planning as a multi-objective problem, presented the basic constraints that should be taken into account during planning, and summarized the technology foundations for radar task planning

Summary

INTRODUCTION

Target tracking and detection by a single radar is challenging [1], [2]. If radars with different advantages are networked to work collaboratively, the target tracking and detection capability of the system can be significantly improved, thanks to performance advantages in systems, polarization modes, and frequency of different radars [3]–[5]. Li et al.: Reliable Task Planning of Networked Devices as Multi-Objective Problem Using NSGA-II and Reinforcement Learning with multi-index constraints was built by Yang to settle the problem of considering multiple functions simultaneously in the condition of performing routine tasks [15] He proposed an optimal allocation method dependent on multi-objective particle swarm optimization based on crowding distance (MOPSO-CD) and a resource-scheduling algorithm based on reinforcement learning. NETWORKED RADAR TASK PLANNING AS A MULTI-OBJECTIVE OPTIMIZATION PROBLEM At present, optimization of networked radar generally considers only a single factor, such as location, power, or time. B. NODE-SELECTION ALGORITHM BASED ON A GREEDY POLICY To perform multi-objective task planning for networked radar, preliminary radar node selection was carried out to reduce solution space and lower algorithm complexity.

NSGA-II ALGORITHM PRINCIPLES AND DESIGN

IMPROVED NSGA-II ALGORITHM

EXPERIMENTAL VALIDATION AND ANALYSIS

Findings

CONCLUSION