Abstract

In this paper, a new optimization algorithm called motion-encoded electric charged particles optimization (ECPO-ME) is developed to find moving targets using unmanned aerial vehicles (UAV). The algorithm is based on the combination of the ECPO (i.e., the base algorithm) with the ME mechanism. This study is directly applicable to a real-world scenario, for instance the movement of a misplaced animal can be detected and subsequently its location can be transmitted to its caretaker. Using Bayesian theory, finding the location of a moving target is formulated as an optimization problem wherein the objective function is to maximize the probability of detecting the target. In the proposed ECPO-ME algorithm, the search trajectory is encoded as a series of UAV motion paths. These paths evolve in each iteration of the ECPO-ME algorithm. The performance of the algorithm is tested for six different scenarios with different characteristics. A statistical analysis is carried out to compare the results obtained from ECPO-ME with other well-known metaheuristics, widely used for benchmarking studies. The results found show that the ECPO-ME has great potential in finding moving targets, since it outperforms the base algorithm (i.e., ECPO) by as much as 2.16%, 5.26%, 7.17%, 14.72%, 0.79% and 3.38% for the investigated scenarios, respectively.

Highlights

  • Unmanned aerial vehicles (UAVs) are among the most promising research tools of interest due to their potentials for use in numerous practical applications [1,2,3].unmanned aerial vehicles (UAV) are especially suitable for surveillance and rescue tasks

  • This paper proposes a motion-encoded electric charged particle optimization (ECPOME) algorithm to solve the problem of moving target search

  • The search path is transformed into a series of motions where the UAV is restricted to the neighbor cells of the current location cell

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Summary

Introduction

UAVs are especially suitable for surveillance and rescue tasks. They are highly capable of working under harsh environmental conditions. In the case of searching for a lost target using UAVs, several factors are taken into consideration. One of those is the ‘golden time’, a critical period when the probability of finding the target becomes maximal [5]. This probability decreases rapidly with time, due to several factors such as the terrain, weather conditions, attenuation of the initial particulars, the target dynamics, etc

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