AOA Estimation Based on MP Algorithm in Beam-Space Using Rotational Invariance Technique

A novel algorithm using Rotational Invariance technique for AOA estimation is presented in this paper, which is based on sparse decomposition of signal, and applied into beam-space against the deficiencies of traditional DF algorithms and sub-space algorithms. The former methods are widely used in actual applications. However, they have many drawbacks such as low-resolution, bad robustness and etc. The latter ones which base on subspace method have gained great interest in the past two decades. What we know about this kind of methods are their super-resolution quality and their weaknesses as well, e.g. constrained by the number of elements, behaving badly when correlativity of sources grows. Approach proposed in this paper fist constructs atom-dictionaries using outputs of second sub-array and then transforms observations of fist sub-array and dictionaries in array-space to beam-space, and finally obtains AOAs through thinning sectors by solving least squares problems. Numerical simulations have proved the effectivity of the algorithm and good performance in low signal-to-noise ratio (SNR) regime.