Optimizing the Multi-Objective Deployment Problem of Mlat System

Abstract

Multilateration (MLAT) systems are powerful means for air traffic surveillance. These systems aim to extract, and display to air traffic controllers identification of aircrafts or vehicles equipped with a transponder. They provide an accurate and real-time data without human intervention using a number of ground receiving stations, placed in some strategic locations around the coverage area, and they are connected with a Central Processing Subsystem (CPS) to compute the target (i.e., aircraft or vehicle) position. The MLAT performance strongly depends on system layout design which consists on deploying the minimum number of stations, in order to obtain the requested system coverage and performance, meeting all the regulatory standards with a minimum cost. In general, choosing the number of stations and their locations to cope with all the requirements is not an obvious task and the system designer has to make several attempts, by trial and error, before obtaining a satisfactory spatial distribution of the stations. In this work we propose a new approach to solve the deployment of Mlat stations problem by focusing on the number of deployed stations and the coverage as the main objectives to optimize. The Non-dominated Sorting Genetic Algorithm II(NSGA-II) was used in order to minimize the total number of stations required to identify all targets in a given area, with the aim to minimize the deployment cost, accelerating processes, and achieve high availability and reliability. The proposed approach is more efficient and converge rapidly which makes it ideal for our research involving optimal deployment of Mlat station.