Abstract
Source localization based on the hybrid time-difference-of-arrival (TDOA) and frequency-difference-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.
Highlights
Localization of a radio frequency (RF) with static and movable sensors has received significant interest in both civil and defense applications, such as search, rescue, and surveillance
Many TDOA/Frequency differences of arrival (FDOA) localization algorithms have been studied in the literatures, e.g., two-step weighted least square (WLS) method, constrained quadratic programming [4], pseudolinear estimation [5] and the constrained weighted least squares (CWLS) method [6]
For a better visibility of sources, we extend our work on path planning of unmanned aerial vehicle (UAV) swarms, which are mounted with TDOA/FDOA sensors
Summary
Localization of a radio frequency (RF) with static and movable sensors has received significant interest in both civil and defense applications, such as search, rescue, and surveillance. Several types of measurements can be used such as time-difference-of-arrival (TDOA) [1], angle-of-arrival (AOA) [2], received signal strength (RSS) [3], or a combination of them. Frequency differences of arrival (FDOA) can be applied to improve localization accuracy, when the source and the sensors are relatively moving. In this paper, we consider the source localization with hybrid TDOA and FDOA measurements. Many TDOA/FDOA localization algorithms have been studied in the literatures, e.g., two-step weighted least square (WLS) method, constrained quadratic programming [4], pseudolinear estimation [5] and the constrained weighted least squares (CWLS) method [6]. Kalman filters based on TDOA and FDOA measurements are applied in [7], [8]
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