Exact and Approximate Maximum Likelihood Localization Algorithms

Chan, Y.T.; Hang, H; Ching, P.C.

doi:10.1109/tvt.2005.861162

Cited by 341 publications

(168 citation statements)

References 9 publications

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“…Weighted LS estimators are computed by introducing a weight matrix into the LS cost function. A favorite LS weight matrix is the inverse of the covariance matrix of TOA measurement errors (Chan et al, 2006a). The general LS cost function for TOA measurements and TDOA measurements are…”

Section: A Toa and Tdoa-based Algorithms With Losmentioning

confidence: 99%

“…The ML approach estimates source position by minimizing the cost function of the probability density function of measurements (Ziskind and Wax, 1988). This algorithm is similar to a weighted nonlinear LS approach when measurement errors are zero-mean Gaussian distributed (Chan et al, 2006a). Li et al (2014) improved on an algorithm published by Chan et al (2006b) using an efficient approximate ML algorithm that included coupling with bad-measurement filters for 3D source localization.…”

Section: A Toa and Tdoa-based Algorithms With Losmentioning

confidence: 99%

“…These LS solutions can provide good initialization for iterative estimators, which converge with less computational effort to a source position estimate with higher accuracy Abel, 1987a andChan et al, 2006a). Some researchers have compared the performance of algorithms (Yu and Oppermann, 2004;Shen et al, 2008;Gezici et al, 2008;and So, 2011).…”

Section: A Toa and Tdoa-based Algorithms With Losmentioning

confidence: 99%

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Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Deng

Rauchenstein

et al. 2016

Review of Scientific Instruments

127

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Locating the position of fixed or mobile sources (i.e., transmitters) based on measurements obtained from sensors (i.e., receivers) is an important research area that is attracting much interest. In this paper, we review several representative localization algorithms that use time of arrivals (TOAs) and time difference of arrivals (TDOAs) to achieve high signal source position estimation accuracy when a transmitter is in the line-of-sight of a receiver. Circular (TOA) and hyperbolic (TDOA) position estimation approaches both use nonlinear equations that relate the known locations of receivers and unknown locations of transmitters. Estimation of the location of transmitters using the standard nonlinear equations may not be very accurate because of receiver location errors, receiver measurement errors, and computational efficiency challenges that result in high computational burdens. Least squares and maximum likelihood based algorithms have become the most popular computational approaches to transmitter location estimation. In this paper, we summarize the computational characteristics and position estimation accuracies of various positioning algorithms. By improving methods for estimating the time-of-arrival of transmissions at receivers and transmitter location estimation algorithms, transmitter location estimation may be applied across a range of applications

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Section: A Toa and Tdoa-based Algorithms With Losmentioning

confidence: 99%

Section: A Toa and Tdoa-based Algorithms With Losmentioning

confidence: 99%

Section: A Toa and Tdoa-based Algorithms With Losmentioning

confidence: 99%

See 1 more Smart Citation

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Deng

Rauchenstein

et al. 2016

Review of Scientific Instruments

127

View full text Add to dashboard Cite

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“…Then one way of obtaining a linear estimate of location is first to solve an LS equation to find an estimate in terms of the reference point and later using the constraint (1) in order to obtain the unconditional location estimate [7,23].…”

Section: Tdoa-based Ls Location Estimationmentioning

confidence: 99%

“…This event can be a consequence of a contemporary failure of a certain sensor or a disturbance in measurements or an erroneous transfer of the measurements. One way of modeling such an outlier is to adopt a MOG noise model for the distance measurement: (23) where i and δ i are zero mean Gaussian random variables with unity standard deviation, q is a positive real number which is much smaller than one and L is a large number, e.g. 100.…”

Section: Non-gaussian Distance Noise and Robust Averagingmentioning

confidence: 99%

Benefits of averaging lateration estimates obtained using overlapped subgroups of sensor data

Altınkaya

2014

Digital Signal Processing

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In this paper, we suggest averaging lateration estimates obtained using overlapped subgroups of distance measurements as opposed to obtaining a single lateration estimate from all of the measurements directly if a redundant number of measurements are available. Least squares based closed form equations are used in the lateration. In the case of Gaussian measurement noise the performances are similar in general and for some subgroup sizes marginal gains are attained. Averaging laterations method becomes especially beneficial if the lateration estimates are classified as useful or not in the presence of outlier measurements whose distributions are modeled by a mixture of Gaussians (MOG) pdf. A new modified trimmed mean robust averager helps to regain the performance loss caused by the outliers. If the measurement noise is Gaussian, large subgroup sizes are preferable. On the contrary, in robust averaging small subgroup sizes are more effective for eliminating measurements highly contaminated with MOG noise. The effect of high-variance noise was almost totally eliminated when robust averaging of estimates is applied to QR decomposition based location estimator. The performance of this estimator is just 1 cm worse in root mean square error compared to the Cramér-Rao lower bound (CRLB) on the variance both for Gaussian and MOG noise cases. Theoretical CRLBs in the case of MOG noise are derived both for time of arrival and time difference of arrival measurement data.

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References

2010

Mobile Positioning and Tracking

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Exact and Approximate Maximum Likelihood Localization Algorithms

Cited by 341 publications

References 9 publications

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Benefits of averaging lateration estimates obtained using overlapped subgroups of sensor data

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