Source Localization Using TDOA and FDOA Measurements in the Presence of Receiver Location Errors: Analysis and Solution

Ho, K. C.; Lu, Xiaoning; Kovavisaruch, La-or

doi:10.1109/tsp.2006.885744

Cited by 507 publications

(339 citation statements)

References 27 publications

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“…The last three constraints are basically obtained from the physical limitations, and they are optional and will be removed if the bounds for are not available. Without loss of information, these three constraints can be combined as (37) With the use of (37), we perform relaxation on (36) and note that has the same effect of to obtain the SDP relaxation algorithm for node localization with unknown propagation speed: s.t. (38) In principle, when noise is absent.…”

Section: Node Localization With Unknown Propagation Speedmentioning

confidence: 99%

“…While in underground WSNs [29] and in-solid scenarios [30] where seismic/vibrational sensor data are processed, the propagation speed is unknown and depends strongly on the propagation medium [31], [32]. In fact, localization of single or noncollaborative sources with anchor location errors have been addressed in [33]- [37] which show that positioning accuracy will be improved when the receiver location uncertainty is taken into account. Recently, a pioneering work for the scenario of WSNs has been presented in [13].…”

Section: Introductionmentioning

confidence: 99%

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Semi-Definite Programming Algorithms for Sensor Network Node Localization With Uncertainties in Anchor Positions and/or Propagation Speed

Lui

et al. 2009

IEEE Trans. Signal Process.

161

112

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Abstract-Finding the positions of nodes in an ad hoc wireless sensor network (WSN) with the use of the incomplete and noisy distance measurements between nodes as well as anchor position information is currently an important and challenging research topic. However, most WSN localization studies have considered that the anchor positions and the signal propagation speed are perfectly known which is not a valid assumption in the underwater and underground scenarios. In this paper, semi-definite programming (SDP) algorithms are devised for node localization in the presence of these uncertainties. The corresponding Cramér-Rao lower bound (CRLB) is also produced. Computer simulations are included to contrast the performance of the proposed algorithms with the conventional SDP method and CRLB.Index Terms-Node localization, range measurements, semi-definite programming, sensor networks.

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Section: Node Localization With Unknown Propagation Speedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semi-Definite Programming Algorithms for Sensor Network Node Localization With Uncertainties in Anchor Positions and/or Propagation Speed

Lui

et al. 2009

IEEE Trans. Signal Process.

161

112

View full text Add to dashboard Cite

show abstract

“…The computational times of the proposed SDR algorithm and [15] are 0.92 s and 0.78 s, respectively. Figure 9 shows the MSEs when there is receiver position uncertainty and the results of [16] are also included. The performance of the proposed scheme is in between [15] and [16], and all cannot attain CRLB.…”

Section: Draft V Simulation Resultsmentioning

confidence: 99%

“…In practice, the receiver positions may not be known perfectly [15] and treating them to be exact may lead to degradation of estimation accuracy [16]- [17]. In this section, we take the receiver position DRAFT uncertainty into account in the algorithm development.…”

Section: B Extension To Receiver Position Uncertaintymentioning

confidence: 99%

A flexible semi-definite programming approach for source localization problems

Chan

et al. 2013

Digital Signal Processing

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In this paper, a new semi-definite programming approach is devised for approximating nonlinear estimation problems. The main idea is to include the noise components as parameters of interests, which increases the flexibility in the convex optimization formulation. Using the source localization as an illustration, we develop semi-definite relaxation (SDR) positioning algorithms using angle-of-arrival, time-of-arrival and time-difference-of-arrival measurements. Numerical examples are included to show the effectiveness of the proposed SDR approach.

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“…Estimating the location of an emitter from a combination of TDOA and FDOA measurements is a highly nonlinear problem. This measurement obtained by a number of sensors is used in an over-determined situation to estimate instantaneous position and velocity of the emitter [6]- [7].…”

mentioning

confidence: 99%

Cramér-Rao Lower Bound Analysis for Many-to-One UAV Tracking Using FDOA Measurements

Min¹,

An²,

Kim³

et al. 2012

IJCEE

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Abstract-This paper considers the application for tracking of one mobile emitter using a sequence of frequency difference of arrival (FDOA) measurements obtained by single pair of receivers at a time. We suppose there is only one unmanned aerial vehicle (UAV) as emitter without data link relation problems, such as disconnection of data link or false measurements. Each FDOA measurement defines a region of possible emitter locations around a unique hyperbola. These analysis leads to the Cramér-Rao lower bound (CRLB) for estimation of movements. Through the analysis we propose the manner for tracking through the error-ellipsoids acquired from each CRLB. Since the performance measure is the length of error ellipsoid axes, we compare the effects of various environments.Index Terms-Frequency difference of arrival (FDOA), Cramér-Rao lower bound (CRLB), unmanned aerial vehicle (UAV), tracking, ground station (GS)

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Source Localization Using TDOA and FDOA Measurements in the Presence of Receiver Location Errors: Analysis and Solution

Cited by 507 publications

References 27 publications

Semi-Definite Programming Algorithms for Sensor Network Node Localization With Uncertainties in Anchor Positions and/or Propagation Speed

Semi-Definite Programming Algorithms for Sensor Network Node Localization With Uncertainties in Anchor Positions and/or Propagation Speed

A flexible semi-definite programming approach for source localization problems

Cramér-Rao Lower Bound Analysis for Many-to-One UAV Tracking Using FDOA Measurements

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