Positioning accuracy when tracking UMTS mobiles in delay and angular dispersive channels

Vidal, Josep; Nájar, Montse; Cabrera, M.; Jativa, R.

doi:10.1109/vetecs.2001.944066

Cited by 11 publications

(5 citation statements)

References 5 publications

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“…This section introduces the minimum variance (MV) TOA estimator, a practical method available in the literature [49], in order to compare its behavior with that described by our CRBs. Remembering that our data is a collection of K channel vector estimates infected with noise, recorded in a time interval of duration KT s as follows:…”

Section: Timing Estimation: the Minimum Variance Methodsmentioning

confidence: 99%

“…Therefore, many channel estimates can be collected in time so as to improve the accuracy of the timing and angle estimates [49].…”

Section: Asmentioning

confidence: 99%

“…From a systemic viewpoint, mobile location may be improved when system dynamics [49] and previous knowledge from the statistics of the measures are used [42] and also from the use of a heterogeneous set of measurements [12,44,49].…”

Section: Coherence Time and Delay Dispersionmentioning

confidence: 99%

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Cramer-Rao bounds in the estimation of time of arrival in fading channels

Jativa

Vidal

2018

EURASIP J. Adv. Signal Process.

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This paper computes the Cramer-Rao bounds for the time of arrival estimation in a multipath Rice and Rayleigh fading scenario, conditioned to the previous estimation of a set of propagation channels, since these channel estimates (correlation between received signal and the pilot sequence) are sufficient statistics in the estimation of delays. Furthermore, channel estimation is a constitutive block in receivers, so we can take advantage of this information to improve timing estimation by using time and space diversity. The received signal is modeled as coming from a scattering environment that disperses the signal both in space and time. Spatial scattering is modeled with a Gaussian distribution and temporal dispersion as an exponential random variable. The impact of the sampling rate, the roll-off factor, the spatial and temporal correlation among channel estimates, the number of channel estimates, and the use of multiple sensors in the antenna at the receiver is studied and related to the mobile subscriber positioning issue. To our knowledge, this model is the only one of its kind as a result of the relationship between the space-time diversity and the accuracy of the timing estimation.

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Section: Timing Estimation: the Minimum Variance Methodsmentioning

confidence: 99%

“…Therefore, many channel estimates can be collected in time so as to improve the accuracy of the timing and angle estimates [49].…”

Section: Asmentioning

confidence: 99%

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Cramer-Rao bounds in the estimation of time of arrival in fading channels

Jativa

Vidal

2018

EURASIP J. Adv. Signal Process.

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“…Furthermore, it must be noted as hybrid techniques exhibit a better behavior than homogeneous ones since it is a well-known principle that errors achieved from a particular positioning technique may be overcome with the application of another one [23]. Positioning accuracy may also take advantage of spatial diversity and mobile system's dynamic [19,20,30]. In fact, Kalman Filter [21,29] and its variants [25,28] have been probed their efficacy by using the mobility dynamics.…”

Section: The Positioning Problemmentioning

confidence: 99%

“…The derivation of these moments is eased by the fact that ξ in (14) is lognormal and therefore, it is related to Ξ as it is shown in (19), and admits to be expressed in relation to a new scaled variable z with mean m z and standard deviation σ z as it is shown in (20) …”

Section: Annex Derivation Of the First Two Moments For The Rms Delaymentioning

confidence: 99%

Mitigación de la Condición NLOS para Sistemas de Posicionamiento de Suscriptor Móvil usando Ponderación de Medidas y Restricciones Geométricas

Játiva

Sanchez

Vidal

2015

Av. Cienc. Ing. (Quito)

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This paper studies the Non-Line-Of-Sight condition mitigation issue in mobile subscriber positioning systems by weighting Time-Of-Arrival measures and applying geometrical restrictions. Particularly, this work departs from a more exact characterization of the signal statistics to achieve weighting factors able to reach a more effective mitigation, and consequently a more accurate mobile subscriber positioning. In addition, restrictions based on the cell geometry are incorporated as a complementary refinement method. Therefore, three new methods with better properties than those taken from the literature and used as reference are introduced. These approaches are evaluated within a realistic simulation scenario.

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