IEEE-TAES2022

The code of the Paper "A Robust TSWLS Localization of Moving Target in Widely Separated MIMO Radars"
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Updated 7 May 2024

A Robust TSWLS Localization of Moving Target in Widely Separated MIMO Radars

This is the simulation code of the following paper:

Jabbari MR, Taban MR, Gazor S. "A robust TSWLS localization of moving target in widely separated MIMO radars." IEEE Transactions on Aerospace and Electronic Systems. 2022 Jul 27;59(2):897-906, DOI: (https://doi.org/10.1109/TAES.2022.3194112)

Abstract

In this article, we investigate the target localization problem in a multiple input multiple output radar systems with widely separated antennas. We derive an accurate and robust closed-form estimator for the target’s location and velocity by using the well-known two-stage weighted least squares technique. We define the nuisance variables in the first stage to obtain a set of pseudo-linear equations and solve them by the WLS estimator. We then approximate the nuisance variables with the first-order Taylor series around the estimates from the previous stage in order to reformulate a set of linear equations, which is solved again using the WLS estimator. Unlike the state-of-the-art methods, the proposed method is robust against the presence of incorrect Doppler shift measurements and perturbations errors imposed by the linear approximations. Simulation results demonstrate that our method outperforms the state-of-the-art methods not only in performance and complexity, but also in robustness.

Content of Code

The main function of this code is "Proposed_Method.m" written in MATLAB 2022b.

License and Referencing

If you in any way use this code for research that results in publications, please cite our paper.

Cite As

MohammadReza Jabbari (Jabari) (2024). IEEE-TAES2022 (https://github.com/morejabbari/IEEE-TAES2022), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2024a
Compatible with any release
Platform Compatibility
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Version Published Release Notes
1.0.0

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To view or report issues in this GitHub add-on, visit the GitHub Repository.