A Robust Framework for DOA Estimation: the Re-Iterative Super-Resolution (RISR) Algorithm

A new approach for spatial direction-of-arrival (DOA) estimation, denoted as Re-Iterative SuperResolution (RISR), is developed based upon a recursive implementation of the minimum mean-square error (MMSE) framework. This recursive strategy alternates between updating an MMSE filter bank according to the previous receive spatial power distribution and then subsequently applying the new filter bank to the received data snapshots to obtain a new estimate of the receive spatial power distribution. Benefits of this approach include robustness to coherent sources such as can occur in multipath environments, operation with very low sample support to enable “tracking” of sources with rapidly changing DOA (e.g. bistatic pulse chasing), intrinsic determination of model order, and robustness to array modeling errors by exploiting approximate knowledge of array calibration tolerances. From an implementation perspective RISR belongs to a class of recursive algorithms that includes Interior Point methods, the minimum-norm based FoCal Underdetermined System Solver (FOCUSS) algorithm, and the Iterative Re-weighted Least Squares (IRLS) algorithm. However, the structure of RISR also enables the natural inclusion of spatial noise covariance information as well as a mechanism to account for array modeling errors which are known to induce degradation for existing super-resolution methods. The inclusion of the latter is also found to facilitate an adaptive form of regularization that establishes a feasible (given model uncertainties) dynamic range for source estimates.