A Novel Closed-Form Estimator for AOA Target Localization Without Prior Knowledge of Noise Variances

This paper addresses the problem of target localization using angle-of-arrival (AOA) measurements when the prior information of the AOA measurement noise variance is unavailable. At first, a maximum likelihood estimator (MLE) and the Cramér–Rao lower bound are derived for the case where the unknown...

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Bibliographic Details
Published in:Circuits, systems, and signal processing systems, and signal processing, 2021-07, Vol.40 (7), p.3573-3591
Main Authors: Pang, Feifei, Wen, Xiangxi
Format: Article
Language:English
Subjects:
Circuits and Systems
Closed form solutions
Cramer-Rao bounds
Electrical Engineering
Electronics and Microelectronics
Engineering
Exact solutions
Instrumentation
Localization
Lower bounds
Mathematical analysis
Maximum likelihood estimators
Noise
Noise measurement
Short Paper
Signal,Image and Speech Processing
Variance
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Summary:This paper addresses the problem of target localization using angle-of-arrival (AOA) measurements when the prior information of the AOA measurement noise variance is unavailable. At first, a maximum likelihood estimator (MLE) and the Cramér–Rao lower bound are derived for the case where the unknown noise variance is a function of the target-to-sensor distance. Then, a novel estimator is proposed to obtain a closed-form solution without the knowledge of noise variance. The proposed estimator can efficiently improve the localization performance by fully exploiting the desirable advantages of the instrumental variable (IV) method and the set of generalized pseudolinear equation. The simulation results show the superior performance of the proposed estimator compared with the MLE, the IV estimator and the pseudolinear estimator.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-020-01624-2