An Approximate ML Estimator for Moving Target Localization in Distributed MIMO Radars

This letter deals with the problem of moving target localization in distributed multiple-input multiple-output (MIMO) radar systems using time delay (TD) and Doppler shift (DS) measurements. The proposed solution to this problem consists of two stages. In the first stage, an initial estimation of ta...

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Bibliographic Details
Published in:IEEE signal processing letters 2020, Vol.27, p.1595-1599
Main Authors: Kazemi, Seyed Amir Reza, Amiri, Rouhollah, Behnia, Fereidoon
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Cramer-Rao bound (CRB)
Doppler effect
Doppler shift (DS)
Localization
Mathematical model
Mathematical models
maximum likelihood (ML) estimation
Maximum likelihood estimation
Maximum likelihood estimators
MIMO (control systems)
Moving targets
multiple-input multiple-output (MIMO) radar
Noise measurement
Numerical methods
Position measurement
Radar equipment
Random noise
Target localization
Task analysis
time delay (TD)
Velocity measurement
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Summary:This letter deals with the problem of moving target localization in distributed multiple-input multiple-output (MIMO) radar systems using time delay (TD) and Doppler shift (DS) measurements. The proposed solution to this problem consists of two stages. In the first stage, an initial estimation of target location is obtained by solving the formulated maximum likelihood (ML) problem based on the TD measurements. In the second stage, by recognizing the obtained position estimate in the previous stage as a priori data and exploiting the DS measurements, another ML problem is formulated, which is efficiently solved via a tractable numerical method to produce a simultaneous estimation of target position and velocity vectors. Overall, the proposed method significantly outperforms the state-of-the-art algorithms under Gaussian noise model as corroborated by numerical simulations.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2020.3020505