Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
IEEE Transactions on Signal Processing
Source Localization and Tracking Using Distributed Asynchronous Sensors
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Source localization with distributed sensor arrays and partial spatial coherence
IEEE Transactions on Signal Processing
Passive Source Localization Using an Airborne Sensor Array in the Presence of Manifold Perturbations
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Self-localization based on ambient signals
ALGOSENSORS'10 Proceedings of the 6th international conference on Algorithms for sensor systems, wireless adhoc networks, and autonomous mobile entities
PANDAA: physical arrangement detection of networked devices through ambient-sound awareness
Proceedings of the 13th international conference on Ubiquitous computing
Refining inaccurate sensor positions using target at unknown location
Signal Processing
Self-Localization based on Ambient Signals
Theoretical Computer Science
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This paper considers the problem of time difference-of-arrival (TDOA) source localization when the TDOA measurements from multiple disjoint sources are subject to the same sensor position displacements from the available sensor positions. This is a challenging problem and closed-form solution with good localization accuracy has yet to be found. This paper proposes an estimator that can achieve this purpose. The proposed algorithm jointly estimates the unknown source and sensor positions to take the advantage that the TDOAs from different sources have the same sensor position displacements. The joint estimation is a highly nonlinear problem due to the coupling of source and sensor positions in the measurement equations. We introduce the novel idea of hypothesized source locations in the algorithm development to enable the formulation of psuedolinear equations, thereby leading to the establishment of closed-form solution for source location estimates. Besides the advantage of closed-form, the newly developed algorithm is shown analytically, under the condition that the TDOA measurement noise and the sensor position errors are sufficiently small, to reach the CRLB accuracy. For clarity, the localization of two disjoint sources is used in the algorithm development. The developed algorithm is then examined under the special case of a single source and extended to the more general case of more than two unknown sources. The theoretical developments are supported by simulations.