Defu Jiang

DOA Estimation Based on Combined Unitary ESPRIT for Coprime MIMO Radar

Direction of arrival (DOA) estimation for coprime multiple-input multiple-output radar is studied, and a combined unitary estimation of signal parameters via rotational invariance technique (ESPRIT)-based algorithm is proposed. The transmitter and the receiver adopt coprime arrays, which are sparse but still uniform. Therefore, unitary ESPRIT is first used to obtain arbitrary ambiguous DOA estimations based on the rotational invariances of transmit and receive arrays, respectively. After recovering all the other estimations, unique DOA estimation is achieved by finding the coincide results from transmit and receive arrays based on the coprimeness. The proposed algorithm obtains more accurate DOA estimation, achieves higher angle resolution, and identifies more targets than conventional methods. Multiple simulations are conducted to verify the improvement of the proposed algorithm.

Sparse representation based two-dimensional direction of arrival estimation using co-prime array

Direction of arrival (DOA) estimation using co-prime array has been attractive for its potential advantages. A co-prime array consists of two uniform linear arrays (ULAs), where one has M elements with

DOA estimation for sparse nested MIMO radar with velocity receive sensor array

N\lambda /2

DOD and DOA estimation for bistatic coprime MIMO radar based on combined ESPRIT

Nλ/2 being the inter-element spacing, and the other has N (co-prime to M) elements with

Extended-Aperture Unitary Root MUSIC-Based DOA Estimation for Coprime Array

M\lambda /2

Direction of arrival estimation using sum co-array of coprime arrays

Mλ/2 being the inter-element spacing . In this paper, the two ULAs of the co-prime array are placed parallel to each other in the same plane for two-dimensional (2D) DOA estimation, and the uniqueness proof of DOA estimation for this geometry is given. By setting the vectorization of the cross covariance matrix of the two ULAs as an observing vector in sparse representation, MN degrees of freedom (DOF) can be achieved via (