IEEE-TAES2022

The code of the Paper "A Robust TSWLS Localization of Moving Target in Widely Separated MIMO Radars"
22 Descargas
Actualizado 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.

Citar como

MohammadReza Jabbari (Jabari) (2025). IEEE-TAES2022 (https://github.com/morejabbari/IEEE-TAES2022), GitHub. Recuperado .

Compatibilidad con la versión de MATLAB
Se creó con R2024a
Compatible con cualquier versión
Compatibilidad con las plataformas
Windows macOS Linux
Etiquetas Añadir etiquetas

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

No se pueden descargar versiones que utilicen la rama predeterminada de GitHub

Versión Publicado Notas de la versión
1.0.0

Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.
Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.