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The problem of direction-of-arrival (DOA) estimation in partly calibrated arrays is addressed. We assume that an array is composed of multiple well-calibrated subarrays of arbitrary known geometry, but there are imperfections between subarrays. We address the cases of unknown (or known with a certain error) intersubarray displacements, imperfect(More)
We consider the problem of source number estimation in the presence of unknown spatially nonuniform noise. Successive array element suppression is applied to isolate the contribution of the noise powers and a likelihood function is derived. When it is combined with the appropriately defined penalty function, a MDL-like criterion is defined. Performance of(More)
Two new approaches to adaptive beamforming in sparse subarray-based partly calibrated sensor arrays are developed. Each subarray is assumed to be well calibrated, so that the steering vectors of all subarrays are exactly known. However, the intersubarray gain and/or phase mismatches are known imperfectly or remain completely unknown. Our first approach is(More)
To overcome the signal-to-interference-and-noise ratio (SINR) performance degradation in the presence of large steering vector mismatches, we propose an iterative robust Capon beamformer (IRCB) with adaptive uncertainty level. The approach iteratively estimates the actual steering vector (SV) based on conventional robust Capon beamformer (RCB) formulation(More)