Localization of Underwater Acoustic Target Using Compressive Sensing on Nested Array

Traditional arrays like Uniform Linear Arrays (ULA) exhibit limited applications in the field of array signal processing because of the requirement of enormous number of sensors and this issue has been solved to some extend by the utilization of sparse arrays. The sparse array uses the concept of difference co-array structures to provide a large increase in degrees of freedom (DoFs) as compared to ULAs for the same number of sensors. Sparse array included in this paper is nested array. Some key DoA estimators like Multiple Signal Classification (MUSIC), Minimum Variance Distortionless Response (MVDR) were used in this work. As a contribution of this paper, concept of compressive sensing is added to the nested array to reduce its computational complexity and to save the benefit of obtaining higher degrees of freedom. This work attempts to produce a new era of DoA estimation by clubbing the concept of nested array and sparse matrix. In short, the paper says that the number of measurements on hand and the number of acoustic sensors available would be enough for a desirable DoA estimation. This work promises a significant reduction in the number of sensors, computational time and complexity, leading to easy processing.