Abstract
Larger array aperture is provided by sparse arrays than uniform ones, which can improve the angle estimation resolution and reduce the cost of system evidently. However, manifold ambiguity is introduced due to the array sparsity. In this paper, a Power Estimation Multiple-Signal Classification (PE-MUSIC) algorithm is proposed to solve the manifold ambiguity of arbitrary sparse arrays for uncorrelated sources in Multiple-Input Multiple-Output (MIMO) radar. First, the paired direction of departure (DOD) and direction of arrival (DOA) are obtained for all targets by MUSIC algorithm, including the true and spurious ones; then, the well-known Davidon–Fletcher–Powell (DFP) algorithm is applied to estimate all targets’ power values, among which the value of a spurious target trends to zero. Therefore, the ambiguity of sparse array in MIMO radar can be cleared. Simulation results verify the effectiveness and feasibility of the method.
Highlights
Multiple-Input Multiple-Output (MIMO) radar employs multiple transmit and receive elements and has the ability to plan transmissions and process received signals jointly
We present a method to solve the ambiguity of sparse array MIMO radar
Substitute all the paired direction of departure (DOD) and direction of arrival (DOA) estimated by Step 1 into the cost function equation (9) to estimate the power values related to all targets through the DFP algorithm. e estimated power value of the spurious target trends to zero
Summary
Multiple-Input Multiple-Output (MIMO) radar employs multiple transmit and receive elements and has the ability to plan transmissions and process received signals jointly. It has been the focus of research owing to its significant performance improvement compared to the conventional phased-array radar [1,2,3]. A lot of methods have been proposed to solve the ambiguity of conventional sparse arrays. Few methods have been proposed to clear the ambiguity in sparse array MIMO radar. We present a method to solve the ambiguity of sparse array MIMO radar. It is separated into two steps by utilizing MUSIC and Davidon–Fletcher–Powell (DFP) algorithms, respectively.
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