Reaching asymptotic efficient performance for squared processing of range and range difference localizations in the presence of sensor position errors

Chen, Shanjie; Ho, K. C.

doi:10.1109/icassp.2014.6853831

Cited by 2 publications

(2 citation statements)

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“…The estimation of a sensor position using distance measurements from a number of anchors is a well known TOA localization problem and the amount of solutions available in the literature is abundant, such as the closed-form two-stage method [27], [28] and the GTRS solution [24], [29]. The two-stage method has a lower noise threshold than the GTRS method but it is much more computationally efficient.…”

Section: A Step-1: Preliminary Solutionmentioning

confidence: 99%

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Accurate Localization of a Rigid Body Using Multiple Sensors and Landmarks

Chen

2015

IEEE Trans. Signal Process.

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This paper develops estimators for locating a rigid body using the time measurements, and the Doppler as well if it is moving, between the sensors in the rigid body and a few landmarks outside. The challenge of rigid body localization is that in addition to the position, we are also interested in obtaining the rotation parameters of the rigid body that must belong to the special orthogonal group. The proposed estimators are non-iterative and have two steps: preliminary and refinement. The preliminary step provides a coarse estimate and the refinement step improves the first step estimate to yield an accurate solution. When the rigid body is stationary, we are able to locate the body with accuracy higher than the solutions of comparable complexity found in the literature. When the rigid body is moving, we develop an estimator that contains the additional unknowns of angular and translational velocities. Simulations show that the proposed estimators, in both stationary and moving cases, can approach the Cramer-Rao Lower Bound performance under Gaussian noise over the small error region.

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Section: A Step-1: Preliminary Solutionmentioning

confidence: 99%

“…Rather than using the above procedure of expressing in terms of and solving through (29), an alternative is to obtain them together by considering as a single unknown in (26) through the minimization of using GTRS, where and . Solving them together can reduce computation if and are large.…”

Section: A Step-1mentioning

confidence: 99%