Detection of Variance Changes and Mean Value Jumps in Measurement Noise for Multipath Mitigation in Urban Navigation

Spangenberg, M.; Calmettes, Vincent; Julien, Olivier; Tourneret, Jean-Yves; Duchâteau, Grégoire

doi:10.1002/j.2161-4296.2010.tb01766.x

Cited by 25 publications

(12 citation statements)

References 14 publications

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“…For example, techniques which utilize cooperative methods to exchange GNSS raw data in order to mitigate the influence of degraded satellite signals can be found in [92] and [93]. Other approaches try to detect jumps of the mean value within the measurement domain over time [94]. Moreover, various forms of receiver autonomous integrity monitoring (RAIM) have been investigated [95].…”

Section: Identificationmentioning

confidence: 99%

“…Via vehicle [90] [14], [27], [56], [68], [91], [94] Via person (No information) [25], [41], [89], [95] Furthermore, this analysis reveals that these projects used a combination of technical designs in order to identify the NLOS signal. For example, [18] used building models, consistency checking, signal geometry and signal strength information within one algorithm.…”

Section: Dynamic Measurementmentioning

confidence: 99%

See 1 more Smart Citation

GNSS Positioning in Non-line-of-Sight Context—a Survey for Technological Innovation

Breßler¹,

Obst²

2017

Adv. sci. technol. eng. syst. j.

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Section: Identificationmentioning

confidence: 99%

Section: Dynamic Measurementmentioning

confidence: 99%

GNSS Positioning in Non-line-of-Sight Context—a Survey for Technological Innovation

Breßler¹,

Obst²

2017

Adv. sci. technol. eng. syst. j.

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“…According to (23), in order to obtain filter innovations based on n measurement equations, several measurement equations (as many measurement equations as the number of bias magnitude samples) have to be processed in parallel and the contributions of all these measurement equations are (23), the following result can be obtained:…”

Section: An Approximate Mlrt Based On Jensen's Inequalitymentioning

confidence: 99%

“…When prior information for this bias is obtained, MP mitigation methods based on Bayesian statistical theory can be considered [22]. For instance, Spangenberg considered in [23] two different models for pseudo-range measurements depending on the availability of LOS signals. Viandier proposed in [24] different ways of handling pseudo-range measurements contaminated by MP biases in urban scenarios, and of estimating the position of a vehicle by using a particle filter applied to a jump Markov system.…”

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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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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“…For instance, [14] proposed to utilize a setup based on two state propagators, alternatively selected based on the Kalman filter’s output, to prevent the risk of using a corrupted propagator for failure detection purposes resulting in an increased failure detection sensitivity. Traditionally, however, the chi-square test is applied for single failure detection and isolation by setting a threshold to judge whether the system is failed or not [15,16]. For instance, [17] employed fuzzy logic and weighted average to deal with the threshold and to reduce the percentage of incorrect alarm effectively.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter

Yang

Mohammadi

Chen

2016

Sensors

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Motivated by the key importance of multi-sensor information fusion algorithms in the state-of-the-art integrated navigation systems due to recent advancements in sensor technologies, telecommunication, and navigation systems, the paper proposes an improved and innovative fault-tolerant fusion framework. An integrated navigation system is considered consisting of four sensory sub-systems, i.e., Strap-down Inertial Navigation System (SINS), Global Navigation System (GPS), the Bei-Dou2 (BD2) and Celestial Navigation System (CNS) navigation sensors. In such multi-sensor applications, on the one hand, the design of an efficient fusion methodology is extremely constrained specially when no information regarding the system’s error characteristics is available. On the other hand, the development of an accurate fault detection and integrity monitoring solution is both challenging and critical. The paper addresses the sensitivity issues of conventional fault detection solutions and the unavailability of a precisely known system model by jointly designing fault detection and information fusion algorithms. In particular, by using ideas from Interacting Multiple Model (IMM) filters, the uncertainty of the system will be adjusted adaptively by model probabilities and using the proposed fuzzy-based fusion framework. The paper also addresses the problem of using corrupted measurements for fault detection purposes by designing a two state propagator chi-square test jointly with the fusion algorithm. Two IMM predictors, running in parallel, are used and alternatively reactivated based on the received information form the fusion filter to increase the reliability and accuracy of the proposed detection solution. With the combination of the IMM and the proposed fusion method, we increase the failure sensitivity of the detection system and, thereby, significantly increase the overall reliability and accuracy of the integrated navigation system. Simulation results indicate that the proposed fault tolerant fusion framework provides superior performance over its traditional counterparts.

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Detection of Variance Changes and Mean Value Jumps in Measurement Noise for Multipath Mitigation in Urban Navigation

Cited by 25 publications

References 14 publications

GNSS Positioning in Non-line-of-Sight Context—a Survey for Technological Innovation

GNSS Positioning in Non-line-of-Sight Context—a Survey for Technological Innovation

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

Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter

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