Sparse recovery by genetic algorithms for acoustic testing applications

Various acoustic testing applications involve solving inverse problems from acoustic measurements, such as acoustic imaging and duct mode identification. In these problems, the physical quantity to be inferred is sparse, which allows applications of certain sparse recovery techniques, such as the compressive sensing. But such methods usually involve very complex theory, which therefore set a high threshold for beginners. By contrast, this work proposes a similar but much simpler method based on the well-known genetic algorithm, which borrows only a few easily understood concepts from nature evolution and can rely on easily accessed software/toolboxes. The performance of the developed method is demonstrated by two acoustic testing experiments in an anechoic chamber, i.e., acoustic imaging and duct mode identification.