Unified Localization Method Without Prior Range Information of Moving Target

This paper proposes a unified localization method based on the time difference of arrival (TDOA) and the frequency difference of arrival (FDOA), which can estimate the position and velocity of the target when it is in the near-field, or the direction of the target when it is in the far-field. We use the modified polar representation (MPR) to unify the far-field and near-field models to construct an optimal localization problem with two constraints and propose an algebraic solution. The method first uses the least squares method to find the initial value without considering the constraints. Then the constraints are used to build an optimization equation to reduce the error of the initial value and obtain the precise value. Simulation results show that the proposed algorithm is close to the Cramer-Rao lower bound (CRLB), verifying the performance.

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Publication Details

Published
2024-08-16
DOI
https://doi.org/10.1109/aces-china62474.2024.10699943
Primary Topic
Target Tracking and Data Fusion in Sensor Networks
Type
article
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article

Unified Localization Method Without Prior Range Information of Moving Target

Yachao Li, Weishuang Gong, Qi Wang
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Target Tracking and Data Fusion in Sensor Networks
article

Unified Localization Method Without Prior Range Information of Moving Target

Yachao Li, Weishuang Gong, Qi Wang
article en

Abstract

This paper proposes a unified localization method based on the time difference of arrival (TDOA) and the frequency difference of arrival (FDOA), which can estimate the position and velocity of the target when it is in the near-field, or the direction of the target when it is in the far-field. We use the modified polar representation (MPR) to unify the far-field and near-field models to construct an optimal localization problem with two constraints and propose an algebraic solution. The method first uses the least squares method to find the initial value without considering the constraints. Then the constraints are used to build an optimization equation to reduce the error of the initial value and obtain the precise value. Simulation results show that the proposed algorithm is close to the Cramer-Rao lower bound (CRLB), verifying the performance.

Xidian University (CN)
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Openalex Percentile: Top 85%
Target Tracking and Data Fusion in Sensor Networks
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