Analysis of sensor‐emitter geometry for emitter localisation using TDOA and FDOA measurements

Kim, Yong Hee; Kim, Dong-Gyu; Han, Jin-Woo; Song, Kyu-Ha; Kim, Hyoung-Nam

doi:10.1049/iet-rsn.2016.0314

Cited by 28 publications

(19 citation statements)

References 26 publications

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“…al. [23], [24] studied the relationship between the linear velocity and localization accuracy. Some fixed sensor geometries and manoeuvre strategies are chosen for localization.…”

Section: Introductionmentioning

confidence: 99%

Optimal Sensor Deployment and Velocity Configuration With Hybrid TDOA and FDOA Measurements

et al. 2019

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Source localization based on the hybrid time-difference-of-arrival (TDOA) and frequencydifference-of-arrival (FDOA) measurements from distributed sensors is an essential problem in wireless sensor networks (WSNs). In this paper, we mainly study the optimal sensor deployment and velocity configuration of UAV swarms mounted with TDOA and FDOA based sensors. Explicit solutions of optimal sensor deployment and velocity configuration are acquired in both static and movable source scenarios based on the Fisher information matrix (FIM). Both centralized and decentralized localization are explored to meet different types of localization methods. Path planning problem of UAV swarms in TDOA/FDOA localization is also studied with constraints. Simulations verify its efficiency with path planning in TDOA and FDOA localization. INDEX TERMS Time-difference-of-arrival (TDOA), frequency-difference-of-arrival (FDOA), sensor deployment, fisher information matrix, path planning.

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“…al. [23], [24] studied the relationship between the linear velocity and localization accuracy. Some fixed sensor geometries and manoeuvre strategies are chosen for localization.…”

Section: Introductionmentioning

confidence: 99%

Optimal Sensor Deployment and Velocity Configuration With Hybrid TDOA and FDOA Measurements

et al. 2019

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“…Several papers concerning CRB-based geometry design problems have been published [6][7][8][9][10][11]. To be concrete, the optimal geometries for time-of-arrival (TOA), TDOA and time-sum-ofarrival (TSOA) localisation systems are studied in [7,8,9], respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, in [6] the optimal geometries for TOA, TDOA, and TSOA localisation systems are studied in a unified framework. What's more, the optimal deployment of moving sensors in TDOA-frequency difference of arrival (FDOA) localisation systems is studied in [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Optimal and fast sensor geometry design method for TDOA localisation systemswith placement constraints

Zhang

Mao

Zhao

et al. 2019

IET signal process.

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The sensor geometry design problem of time difference of arrival (TDOA) localisation systems based on Cramer-Rao bound is studied. Sensor placement constraints are considered, which means the available placement area is limited. This makes sensor geometry design a sensor selection problem. In two-dimensional (2D) TDOA localisation or 3D TDOA localisation on the earth surface, sensor selection can be implemented through solving a fractional integer programming problem. However, traditional fractional integer programming methods are either suboptimal or too time consuming. For this reason, a new method named path-varying sphere decoding is proposed in two steps. In step one, the programming problem is relaxed into two sphere decoding (SD) subproblems. Solving these subproblems leads to the optimal solution, and the required computational complexity is much less than those of traditional optimal methods. In step two, the structure of the cost function is explored. This makes it possible to calculate the path-varying upper-bound of a quadratic function. Thus the quadratic function constraint used in one SD subproblem becomes tighter and the calculation speed is enhanced. Theory analyses and simulation results show that the proposed method is not only optimal but also much faster than traditional optimal methods when solving large-scale programming problems.

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“…In addition, the positioning methods combining two kinds of measurements such as TDOA/FDOA and TDOA/DOA are also widely discussed [16,17]. Except for reducing the number of signals required, the combination of time and frequency measurements is attractive for improvement of positioning accuracy [18,19]. In these methods, however, only the information extracted from the received signals is used for positioning.…”

Section: Introductionmentioning

confidence: 99%

A Vessel Positioning Algorithm Based on Satellite Automatic Identification System

Wang

Meng

et al. 2017

Journal of Electrical and Computer Engineering

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Vessels can obtain high precision positioning by using the global navigation satellite system (GNSS), but when the ship borne GNSS receiver fails, the existence of an alternative positioning system is important for the navigation safety of vessel. In this paper, a localization method based on the signals transmitted by satellite-based automatic identification system (AIS) is proposed for vessel in GNSS-denied environments. In the proposed method, the positioning model is a modification on the basis of time difference and frequency difference of arrival measurements by introducing an additional measurement, and the measurement is obtained through the interactive multiple model algorithm. The performance of the proposed strategy is evaluated through simulations, and the results validate the feasibility and reliability of vessel localization based on satellite-based AIS.

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Analysis of sensor‐emitter geometry for emitter localisation using TDOA and FDOA measurements

Cited by 28 publications

References 26 publications

Optimal Sensor Deployment and Velocity Configuration With Hybrid TDOA and FDOA Measurements

Optimal Sensor Deployment and Velocity Configuration With Hybrid TDOA and FDOA Measurements

Optimal and fast sensor geometry design method for TDOA localisation systemswith placement constraints

A Vessel Positioning Algorithm Based on Satellite Automatic Identification System

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