5 accepted papers
The Partial Relaxation framework has recently been introduced to address the Direction-of-Arrival (DOA) estimation problem [1]–[3]. DOA estimators under the Partial Relaxation (PR) framework are computationally efficient while preserving excellent DOA estimation accuracy. This is achieved by keeping…
In practical applications, non-Gaussianity of the signal at the sensor array is detrimental to the performance of conventional Direction-of-Arrival (DOA) estimators developed under the Gaussian model. In this paper, we propose a novel robust DOA estimator from the data collected at the sensor array…
A family of computationally efficient DOA estimators under the partial relaxation framework has recently been proposed. In this framework, the manifold structure of the "interfering" signals is relaxed, and only the manifold structure of one desired signal is retained. This particular type of relaxa…
In this paper, the Cramér-Rao Bound for the Direction-ofArrival parameter under the partial relaxation framework is derived. We introduce a non-redundant parameterization of the signal model corresponding to the partial relaxation framework, in which the array structure in part of the steering matri…
In the partial relaxation approach, at each desired direction, the manifold structure of the remaining interfering signals impinging on the sensor array is relaxed, which results in closed form estimates for the interference parameters. By adopting this approach, in this paper, a new estimator based…