Genetic programming based Choquet integral for multi-source fusion

• Main Authors: Simth, Ryan E., Anderson, Derek T., Zare, Alina, Ball, John E., Smock, Brandon, Fairley, Josh R., Howington, Stacy E.
• Format: Conference Proceeding
• Language: English
• Subjects: Choquet integral; Frequency modulation; fuzzy integral; genetic algorithm; Genetic algorithms; genetic program; Genetics; Ground penetrating radar; multi-sensor fusion; Next generation networking; Sensors; Signal processing algorithms
• ISSN: 1558-4739
• DOI: 10.1109/FUZZ-IEEE.2017.8015481

While the Choquet integral (Chi) is a powerful parametric nonlinear aggregation function, it has limited scope and is not a universal function generator. Herein, we focus on a class of problems that are outside the scope of a single Chi. Namely, we are interested in tasks where different subsets of inputs require different Chls. Herein, a genetic program (GF) is used to extend the Chi, referred to as GpChI hereafter, specifically in terms of compositions of Chls and/or arithmetic combinations of Chls. An algorithm is put forth to leam the different GP Chls via genetic algorithm (GA) optimization. Synthetic experiments demonstrate GpChI in a controlled fashion, i.e., we know the answer and can compare what is learned to the truth. Real-world experiments are also provided for the mult-sensor fusion of electromagnetic induction (EMI) and ground penetrating radar (GPR) for explosive hazard detection. Our mutli-sensor fusion experiments show that there is utility in changing aggregation strategy per different subsets of inputs (sensors or algorithms) and fusing those results.