An apparatus comprising: a radar circuit (150, 160) to receive reflection signals, in response to transmitted radar signals, as reflections from objects; and computer processing circuitry with a first module to process data corresponding to the reflection signals via a sparse array and, in response, to generate output data indicative of signal magnitude associated with the reflection signals, and a second module to discern angle-of-arrival information for the output data by performing the following steps: computing a correlation vector based on the output data (r K) and a plurality of spatial frequency support vectors (e j2πf1t1... e j2πf1tN, e j2πf2t1... e j2πf2tN,..., e j2πfMt1... e j2πfMtN) each steering to a dedicated support special frequency (f 1, f 2,..., f M), where (t 1,... t N) relates to the spatial sampling positions, identifying at least one spatial frequency support vector indicative of a correlation peak for the output data, generating upper-side and lower-side support vectors with spatial frequencies defined as the means spatial frequencies between the identified spatial frequency support vector and its two closest neighbors along a spatial frequency spectrum of the plurality of spatial frequency support vectors, and providing, via correlation of the upper-side and lower-side support vectors and said at least one spatial frequency support vector, at least one new vector that is more refined along the spatial frequency spectrum for said at least one spatial frequency support vector. a radar circuit (150, 160) to receive reflection signals, in response to transmitted radar signals, as reflections from objects; and computer processing circuitry with a first module to process data corresponding to the reflection signals via a sparse array and, in response, to generate output data indicative of signal magnitude associated with the reflection signals, and a second module to discern angle-of-arrival information for the output data by performing the following steps: computing a correlation vector based on the output data (r K) and a plurality of spatial frequency support vectors (e j2πf1t1... e j2πf1tN, e j2πf2t1... e j2πf2tN,..., e j2πfMt1... e j2πfMtN) each steering to a dedicated support special frequency (f 1, f 2,..., f M), where (t 1,... t N) relates to the spatial sampling positions, identifying at least one spatial frequency support vector indicative of a correlation peak for the output data, generating upper-side and lower-side support vectors with spatial frequencies defined as the means spatial frequencies between the identified spatial frequency support vector and its two closest neighbors along a spatial frequency spectrum of the plurality of spatial frequency support vectors, and providing, via correlation of the upper-side and lower-side support vectors and said at least one spatial frequency support vector, at least one new vector that is more refined along the spatial frequency spectrum for said at least one spatial frequency support vector. a first module to process data corresponding to the reflection signals via a sparse array and, in response, to generate output data indicative of signal magnitude associated with the reflection signals, and a second module to discern angle-of-arrival information for the output data by performing the following steps: computing a correlation vector based on the output data (r K) and a plurality of spatial frequency support vectors (e j2πf1t1... e j2πf1tN, e j2πf2t1... e j2πf2tN,..., e j2πfMt1... e j2πfMtN) each steering to a dedicated support special frequency (f 1, f 2,..., f M), where (t 1,... t N) relates to the spatial sampling positions, identifying at least one spatial frequency support vector indicative of a correlation peak for the output data, generating upper-side and lower-side support vectors with spatial frequencies defined as the means spatial frequencies between the identified spatial frequency support vector and its two closest neighbors along a spatial frequency spectrum of the plurality of spatial frequency support vectors, and providing, via correlation of the upper-side and lower-side support vectors and said at least one spatial frequency support vector, at least one new vector that is more refined along the spatial frequency spectrum for said at least one spatial frequency support vector. computing a correlation vector based on the output data (r K) and a plurality of spatial frequency support vectors (e j2πf1t1... e j2πf1tN, e j2πf2t1... e j2πf2tN,..., e j2πfMt1... e j2πfMtN) each steering to a dedicated support special frequency (f 1, f 2,..., f M), where (t 1,... t N) relates to the spatial sampling positions, identifying at least one spatial frequency support vector indicative of a correlation peak for the output data, generating upper-side and lower-side support vectors with spatial frequencies defined as the means spatial frequencies between the identified spatial frequency support vector and its two closest neighbors along a spatial frequency spectrum of the plurality of spatial frequency support vectors, and providing, via correlation of the upper-side and lower-side support vectors and said at least one spatial frequency support vector, at least one new vector that is more refined along the spatial frequency spectrum for said at least one spatial frequency support vector.