Optimum sparse array configuration for DOA estimation on moving platforms

Sparse array motion can efficiently expand the numbers of achievable degrees of freedom (DOFs) and consecutive lags, improving direction-of-arrival (DOA) estimation. Sparse arrays on a moving platform benefit from motion translation that introduces new sensor positions, which collectively with the original positions can increase the number of spatial autocorrelation lags and lead to full array augmentability. This property has been recently used for the case of environment-independent sparse array configurations, such as those defined by nested and co-prime arrays. In this paper, we consider environment-dependent sparse arrays (EDSAs) design using Cramer-Rao bound (CRB) as the metric of optimality for DOA estimation. The CRB is derived for a sparse array on a moving platform, where the number of identifiable uncorrelated sources exceeds the number of sensors. The CRB expression is used to solve for the sparse array configuration by applying the Genetic algorithm. Simulation results are provided to validate the effectiveness of the proposed EDSA design. (C) 2020 Elsevier Inc. All rights reserved.