A Method for Angular Super-Resolution via Big Data Radar System

Abstract

This paper proposes a novel method for enhancing angular resolution in multimedia big data navigation radar system. A new radar scanning model is designed on the basis of quadratic programming theory, by which the proposed Gradient Projection (GP) algorithm is used for solving the optimal solution of this model, and then the target information can be restored successfully at low signal to noise ratio (SNR). Simulations further confirm our theoretical discussion, and manifest that the efficiency and applicability of the proposed method is favorable that the resolution ratio reaches 4~11 times under our proposed scanning model framework if SNR is above 10dB. Moreover, the designed model is suitable for some other angular super-resolution methods, the restoration ratio of which can be improved while SNR is be equal or greater than 10dB. In this case, a higher signal to reconstructed error ratio (SRER) is provided by our method.