Meta-heuristics for Improved RF Emitter Localization
Locating Radio Frequency (RF) emitters can be done with a number of methods, but cheap and widely available sensors make the Power Difference of Arrival (PDOA) technique a prominent choice. Predicting the location of an unknown RF emitter can be seen as a continuous optimization problem, minimizing the error w.r.t. the sensor measurements gathered. Most instances of this problem feature multi-modality, making these challenging to solve. This paper presents an analysis of the performance of evolutionary computation and other meta-heuristic methods on this real-world problem. We applied the Nelder-Mead method, Genetic Algorithm, Covariance Matrix Adaptation Evolutionary Strategies, Particle Swarm Optimization and Differential Evolution. The use of meta-heuristics solved the minimization problem more efficiently and precisely, compared to brute force search, potentially allowing for a more widespread use of the PDOA method. To compare algorithms two different metrics were proposed: average distance miss and median distance miss, giving insight into the algorithms’ performance. Finally, the use of an adaptive mutation step proved important.
KeywordsSearch heuristics Continuous optimization Multilateration
We would like to thank Ingebjørg Kåsen and Eilif Solberg for their assistance with the statistical issues in this paper and Jørgen Nordmoen for enlightening discussions and excellent feedback.
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