Low-complexity compressive sensing based DOA estimation for co-prime arrays
Qing Shen, Wei Liu, Wei Cui, Siliang Wu · 2014
A low-complexity direction-of-arrival (DOA) estimation method is proposed based on the recently proposed co-prime array structure. In an existing method, a virtual array model is generated by directly vectorizing the covariance matrix and then a sparse signal recovery method is used to obtain the DOA estimation result. However, there are a large number of redundant entries in the covariance matrix and they can be combined together to form a model with a significantly reduced dimension, therefore leading to a solution with much lower computational complexity without sacrificing its performance. A further reduction in complexity is achieved by considering that the estimation result for noise power is far from its real value especially in scenarios with low input signal to noise ratio (SNR) and therefore can be removed from the formulation.