Application of Evolution Strategies to the Design of Tracking Filters with a Large Number of Specifications

Jesús García Herrero,José R Casar Corredera,José M Molina López,Gonzalo De Miguel Vela,Juan A Besada Portas,Antonio Berlanga De Jesús

doi:10.1155/s1110865703302057

Abstract

This paper describes the application of evolution strategies to the design of interacting multiple model (IMM) tracking filters in order to fulfill a large table of performance specifications. These specifications define the desired filter performance in a thorough set of selected test scenarios, for different figures of merit and input conditions, imposing hundreds of performance goals. The design problem is stated as a numeric search in the filter parameters space to attain all specifications or at least minimize, in a compromise, the excess over some specifications as much as possible, applying global optimization techniques coming from evolutionary computation field. Besides, a new methodology is proposed to integrate specifications in a fitness function able to effectively guide the search to suitable solutions. The method has been applied to the design of an IMM tracker for a real-world civil air traffic control application: the accomplishment of specifications defined for the future European ARTAS system.

Highlights

A tracking filter has the double goal of reducing measurement noise and consistently predicting future values of signal
The design of tracking filters for the air traffic control (ATC) problem demands complex algorithms, like the modern interacting multiple model (IMM) filter [1]. These algorithms depend on a high number of parameters which must be adjusted in order to achieve, as much as possible, the desired tracking filter performance
We have described a methodology based on evolution strategies (ES) for the design of IMM-tracker techniques to accomplish a considerably large set of predefined specifications

Summary

Introduction

A tracking filter has the double goal of reducing measurement noise and consistently predicting future values of signal. This kind of problems has efficient solutions in the case of stationary signals, but solutions for nonstationary problems are not so consolidated yet. The design of tracking filters for the ATC problem demands complex algorithms, like the modern interacting multiple model (IMM) filter [1]. These algorithms depend on a high number of parameters (seven in the IMM design presented here) which must be adjusted in order to achieve, as much as possible, the desired tracking filter performance. No direct design methodology has been proposed to generate the best solution for a specific application to date, apart from manual parameterization and evaluation with simulation

Methods

Results

Conclusion