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Elliptic localization is an active range-based positioning technique that employs multiple transmitter-receiver pairs, each of which is able to provide separate bistatic range (BR) measurement. In this letter, an algebraic closed-form method for locating a single target from BR measurements using a distributed multiple-input multiple-output (MIMO) radar system is proposed. First, a set of linear equations is established by eliminating the nuisance parameters, and then, a weighted least squares estimator is employed to obtain the target position estimate. To refine the localization performance, the error in the initial solution is estimated in the sequence. The proposed method is shown... 

In this letter, the problem of target localization from bistatic range (BR) measurements in distributed multiple-input multiple-output radar systems is investigated. By introducing nuisance parameters, a pseudolinear set of BR equations is established. Then, a closed-form localization algorithm is developed, based on this pseudolinear set of equations and multistage weighted least squares estimation. This solution is shown analytically to attain the Cramer-Rao lower bound under the small Gaussian noise assumption. Simulations are included to support the theoretical studies. Unlike the existing studies where the variance of BR measurements is assumed to be independent of the corresponding... 

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... 

In this paper, an algebraic solution for elliptic localization in multiple-input multiple-output (MIMO) radar systems is proposed by taking the uncertainties in antenna positions and their clock parameters into account. Through nonlinear transformation and multi-stage processing, the target position is estimated in closed-form by successively utilizing the weighted least squares estimator. Theoretical analysis demonstrates that the proposed method is able to attain the Cramer-Rao lower bound (CRLB) performance under mild Gaussian error. The conditions for achieving the CRLB are derived as well. The numerical simulations confirm the analytical results and demonstrate significant performance... 

Multiple-input multiple-output (MIMO) radars offer higher resolution, better target detection, and more accurate target parameter estimation. Due to the sparsity of the targets in space-velocity domain, we can exploit Compressive Sensing (CS) to improve the performance of MIMO radars when the sampling rate is much less than the Nyquist rate. In distributed MIMO radars, block CS methods can be used instead of classical CS ones for more performance improvement, because the received signal in this group of MIMO radars is a block sparse signal in a basis. In this paper, two new methods are proposed to improve the performance of the block CS-based distributed MIMO radars. The first one is a new... 

This study presents and analyses a novel joint detection and parameters estimation of multiple targets using a co-located multiple input/multiple output radar with moving platform, in the presence of clutter and background noise. A notable feature of the proposed approach is that clutter cancellation is independent of received data. Moreover, to decrease the computational load, the authors propose to perform the target detection and parameters estimation by directly processing on the compressive measurements as opposed to Nyquist samples. © The Institution of Engineering and Technology