Abstract
Elliptic localization is a range-based positioning technique exploiting multiple transmitter-receiver pairs, each of which provides separate bistatic range (BR) measurements. In this paper, a novel computationally efficient solution for locating a single target from BR measurements in distributed MIMO radar systems is proposed. Due to nonconvex nature of the associated maximum likelihood (ML) estimation problem, its globally optimal solution is difficult to obtain. We first reformulate the ML estimation as a nonconvex constrained weighted least squares problem. Owing to special structure of the resulting problem, it is recast as a convex problem, whose exact solution can be obtained in closed-form or near closed-form manner. Moreover, the proposed method is extended to localization in the presence of antenna location uncertainties. The positioning performance of the proposed method is shown to achieve the CRLB up to relatively high noise levels. Furthermore, numerical simulations demonstrate a significant performance improvement of the proposed method over the state-of-the-art algorithms.
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