Sparse array acoustic holography utilizing convex optimization over the direct product of object space and observed signal space

摘要

This paper proposes a novel blind target identification algorithm and shows experimental results obtained by a proof-of-concept (POC) model, a 3-D sparse array holographic sonar, which has a limited number of transducers distributed sparsely. The proposed algorithm is based on convex optimization over the direct product of the object space (is contained in R{sup}N) and the observed signal space (is contained in R{sup}M) By acoustical experiments, it is proved that the proposed algorithm has the following improvements: (1) Targets can be identified when unknown components exist in the transfer function. (2) Transient behavior of the convergence becomes more stable than that of POCS algorithm. (3) Instability caused by the lack of information about the transfer function can be reduced. (4) Artifacts caused by the spurious lobes can be reduced under the condition that the inter-spacing of tranceducer elements is larger than the wave length.