Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Exploiting geometry for improved hybrid AOA/TDOA-based localization
Signal Processing
Robotics and Autonomous Systems
Energy efficient transmission scheduling for infrastructure sensor nodes in location systems
Computer Networks: The International Journal of Computer and Telecommunications Networking
Real-time localization of an UAV using Kalman filter and a Wireless Sensor Network
Journal of Intelligent and Robotic Systems
TDOA-based adaptive sensing in multi-agent cooperative target tracking
Signal Processing
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The paper presents a simple recursive solution to passive tracking of maneuvering targets using time difference of arrival (TDOA) measurements. Firstly, an iterative Gauss-Newton algorithm is developed for stationary target localization based on a constrained weighted least-squares (CWLS) criterion. The advantages of the CWLS estimate are its inherent stability due to the absence of local minima at infinity and its capability to match the performance of the maximum-likelihood (ML) estimate. To track maneuvering targets, a computationally efficient recursive least-squares (RLS) algorithm is developed, which smoothes successive stationary target location estimates obtained from the ML or CWLS solution using a constant-acceleration motion model. In simulation studies, the proposed recursive tracking algorithm is compared with a Kalman tracking algorithm that estimates the target track directly from the TDOA measurements, and is shown to be capable of outperforming the Kalman tracker.