Dynamic direction-of-arrival estimation via spatial compressive sensing

This work addresses a problem of dynamic spatial spectrum estimation using linear sensor array via spatial compressive sensing (SCS). Static SCS approach is generalized for scenarios with dynamic far-field sources via Kalman filter, which provides the noncoherent minimum mean squared integration of instantaneous spatial spectrum estimations. The proposed dynamic SCS approach (DSCS) is shown to be efficient in low-SNR scenarios when the spatial spectrum is nonsparse, and therefore, the static SCS-based method cannot be used. Performance of the DSCS was evaluated in bearing-only target tracking scenarios where maneuvering target was considered.