Multipath Estimation in Multicorrelator GNSS Receivers using the Maximum Likelihood Principle

Blanco-Delgado, Nuria; Nunes, Fernando D.

doi:10.1109/taes.2012.6324696

Cited by 46 publications

(24 citation statements)

References 13 publications

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“…According to the Kalman filter theory, the denominator of (14) which is the likelihood function associated with the hypothesis H 0 can be defined as After replacing (17) and (18) in (14), the MLRT test statistic based on the Monte Carlo integration can be expressed as follows:…”

Section: An Approximate Mlrt Based On Jensen's Inequalitymentioning

confidence: 99%

“…Considering that the GNSS receiver has to track the signal composed of the direct signal and of delayed reflections in the LOS situation, several MP mitigation methods based on the narrow correlator delay lock loop [5] have been proposed, such as the strobe correlator [6], the early-late-slope technique [7], the double-delta correlator [8] and the MP insensitive delay lock loop [9]. Moreover, the direct and reflected signal parameters can be estimated by using a robust statistical approach based on maximum likelihood principle [10][11][12][13][14]. Bayesian approaches have also been proposed to estimate the MP parameters within GNSS receivers since they allow the nonlinear estimation problem to be handled [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Detecting, estimating and correcting multipath biases affecting GNSS signals using a marginalized likelihood ratio-based method

et al. 2016

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Calmettes. Detecting, estimating and correcting multipath biases affecting GNSS signals using a marginalized likelihood ratio-based method. Signal Processing, Elsevier, 2016, vol. 118, pp. 221-234 a b s t r a c tIn urban canyons, non-line-of-sight (NLOS) multipath interferences affect position estimation based on global navigation satellite systems (GNSS). This paper proposes to model the effects of NLOS multipath interferences as mean value jumps contaminating the GNSS pseudo-range measurements. The marginalized likelihood ratio test (MLRT) is then investigated to detect, identify and estimate the corresponding NLOS multipath biases. However, the MLRT test statistics is difficult to compute. In this work, we consider a Monte Carlo integration technique based on bias magnitude sampling. Jensen's inequality allows this Monte Carlo integration to be simplified. The multiple model algorithm is also used to update the prior information for each bias magnitude sample. Some strategies are designed for estimating and correcting the NLOS multipath biases. In order to demonstrate the performance of the MLRT, experiments allowing several localization methods to be compared are performed. Finally, results from a measurement campaign conducted in an urban canyon are presented in order to evaluate the performance of the proposed algorithm in a representative environment.

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Section: An Approximate Mlrt Based On Jensen's Inequalitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting, estimating and correcting multipath biases affecting GNSS signals using a marginalized likelihood ratio-based method

et al. 2016

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“…This maximization is not straightforward since all terms in h1(·) are related to the unknown MP signal parameters. Thus we propose an interval grid search based on the maximum likelihood principle to perform the estimation of MP parameters [5,17]. Finally, the estimator of the mth MP parameter vector can be expressed as followŝ…”

Section: Step 1: Mp Parameter Estimation Based On the Mlementioning

confidence: 99%

“…Several estimation methods proposed in recent years are based on the maximum likelihood principle [3][4][5], such as the vision correlator (VC) [6] and the fast iterative maximum likelihood algorithm (FIMLA) [7]. However, the maximum likelihood-based approaches assume that the signal parameters are not time varying and do not exploit any dynamic information for the signal parameters.…”

Section: Introductionmentioning

confidence: 99%

A maximum likelihood-based unscented Kalman filter for multipath mitigation in a multi-correlator based GNSS receiver

Cheng

Calmettes

Tourneret

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. ABSTRACTIn complex environments, the presence or absence of multipath signals not only depends on the relative motion between the GNSS receiver and navigation satellites, but also on the environment where the receiver is located. Thus it is difficult to use a specific propagation model to accurately capture the dynamics of multipath signal parameters when the GNSS receiver is moving in urban canyons or other severe obstructions. This paper introduces a statistical model for the line-of-sight and multipath signals received by a GNSS receiver. A multi-correlator based GNSS receiver is also exploited with the advantage to fully characterizing the impact of multipath signals on the correlation function by providing samples of the whole correlation function. Finally, a maximum likelihood-based unscented Kalman filter is investigated to estimate the line-of-sight and multipath signal parameters. Numerical simulations clearly validate the effectiveness of the proposed approach.

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“…The very first one is the multipath estimating delay lock loop (MEDLL) which estimates the signal parameters by inducing multiple correlators [14]. The following studies simplify the estimation methods and reduce the computational loads such as the data compression based multipath mitigation technology [15], the fast iterative Maximum-Likelihood algorithm [16] and the log-likelihood function maximization based low computational complexity multipath estimation approach [17]. Now, it has been validated that these parameter estimation based methods are easy to be realized and suitable for the real time application.…”

Section: Introductionmentioning

confidence: 99%

Multiple carrier correlators based carrier phase multipath mitigation technique for real time kinematic

Ruan

Liu

2016

2016 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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Mitigating carrier phase multipath errors continue to be a significant challenge for Real Time Kinematic (RTK) using Global Navigation Satellite Systems (GNSS). As the multipath error is dependent on the operational environment and therefore cannot be mitigated by differencing techniques, we propose a Multiple Carrier Correlators based carrier phase multipath mitigation technique (MCC) for the high precise positioning in the environment with multipath. In the method, two extra carrier correlators at the phases of pi/4 and -pi/4 are introduced and a maximum likelihood estimator is developed based on these correlator outputs to estimate the carrier phase of the line-of-sight signal. A Monte Carlo simulation is carried out to evaluate the carrier phase measuring accuracy of the proposed method under multipath. The experiments using real GNSS signal data are also conducted in a certain multipath scenario and an urban canyon area with random multipath. The results show the proposed method can greatly improve the performance of Real Time Kinematic positioning in the situation with multipath and outperforms the traditional multicorrelator based carrier phase multipath mitigation method.

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Multipath Estimation in Multicorrelator GNSS Receivers using the Maximum Likelihood Principle

Cited by 46 publications

References 13 publications

Detecting, estimating and correcting multipath biases affecting GNSS signals using a marginalized likelihood ratio-based method

Detecting, estimating and correcting multipath biases affecting GNSS signals using a marginalized likelihood ratio-based method

A maximum likelihood-based unscented Kalman filter for multipath mitigation in a multi-correlator based GNSS receiver

Multiple carrier correlators based carrier phase multipath mitigation technique for real time kinematic

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