OS-CFAR and CMLD threshold optimization in distributed systems using evolutionary strategies

This paper proposes an improvement of the threshold optimization in distributed ordered statistics constant false alarm rate and censored mean level detector using Evolutionary Strategies (ESs). The target is assumed to be Rayleigh distributed and the observations are independent from sensor to sensor. Two fusion rules; "AND" and "OR" were considered. An ES was tested and a comparison with a genetic algorithm improved by a tournament selection was also analyzed. Among a variety of evolution strategies, the most popular proposed in the literature are the strategy (mu, lambda) and the strategy (mu + lambda). We proposed an (mu + lambda) evolution strategy, by which a self-adaptation mutation is used. The results showed that, although the ES is more difficult to implement and is in a certain manner slower than the GA, it improves the performance of the system.