Hybrid Cooperative Vehicle Positioning Using Distributed Randomized Sigma Point Belief Propagation on Non-Gaussian Noise Distribution

Georges, Hassana Maigary; Xiao, Zhu

doi:10.1109/jsen.2016.2602847

Cited by 32 publications

(14 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…α as the measurement errors of the elevation angle between two nodes are opposite after using (3). Please notice the difference between P…”

Section: B Cooperative Fusionmentioning

confidence: 99%

“…The Global Positioning System (GPS) itself is not enough in IoT applications because in these complicated environments (such as the urban areas and the buildings), the satellite signal can be blocked sometimes that might lead to very large errors or even localization suspend [7], [8]. Thus, different types of localization systems or methods are developed to overcome the weaknesses of satellite positioning system, such as using Wireless Sensor Network (WSN) [6], [9]- [14], base-station [15]- [19], Wi-Fi/Bluetooth [1], [2], [20]- [22] and ultra-wide bandwidth (UWB) signal [3], [22]- [26]. Hybrid localization which fuses massive observations measured by different signal sources could provide higher localization accuracy and stronger robustness.…”

Section: Introductionmentioning

confidence: 99%

“…Hybrid localization which fuses massive observations measured by different signal sources could provide higher localization accuracy and stronger robustness. Therefore, hybrid localization by multisensors is popular in recent years [3], [4], [12], [16], [20], [22], [27]- [30].…”

Section: Introductionmentioning

confidence: 99%

“…Nodes are also required to share their own information in the cooperative network. For example, distributed belief propagation based algorithms, which usually use factor graph to represent the relationship of the collaborating information, have been proposed in [3], [24], [27], [34]. For the centralized one, all information from each node is gathered in a unique process unit and corresponding positioning algorithms are used to calculate the fusion result [11], [13], [31].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Intelligent Multisensor Cooperative Localization Under Cooperative Redundancy Validation

Yin

Deng

2021

IEEE Trans. Cybern.

View full text Add to dashboard Cite

Localization plays a key role in Internet of Things (IoT). This paper proposes a novel intelligent cooperative multisensor localization method called Edge Cloud Cooperative Localization (ECCL) which has the range and angle observations from the neighbour nodes along with the location observations from an absolute coordinate localization system like Global Positioning System (GPS). The edge cloud structure is proposed which employs several distributed Kalman Filters (KFs) in sensor nodes edge and a centralized cooperative fusion unit in the cloud. For a robust fusion, a Cooperative Redundancy Validation (CRV) method is proposed to detect the outliers. The proposed ECCL scheme has the advantages of both distributed and centralized localization, which satisfies the needs of high reliability and high accuracy, especially when sensor nodes have limited computational resources. The simulation and experiment results show that our proposed ECCL algorithm outperforms the other schemes both in outlier detection and localization accuracy.

show abstract

“…α as the measurement errors of the elevation angle between two nodes are opposite after using (3). Please notice the difference between P…”

Section: B Cooperative Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Multisensor Cooperative Localization Under Cooperative Redundancy Validation

Yin

Deng

2021

IEEE Trans. Cybern.

View full text Add to dashboard Cite

show abstract

“…Georges et al [4] proposed a sigma point belief propagation algorithm for the sensor fusion of pseudorange measurements from GNSS and Ultra-Wide Band (UWB) signals. The algorithm was designed for localization of vehicles in urban areas with Non-line-of-sight (NLOS) conditions and non-Gaussian distributed noise.…”

Section: Introductionmentioning

confidence: 99%

Precise Positioning in Alpine Areas with Troposphere and Multipath Estimation

Henkel

Banjara

2018

IEEE Sensors J.

View full text Add to dashboard Cite

Real-time kinematic (RTK) positioning with Global Navigation Satellite System (GNSS) signals is widely used, e.g., for surveying, agriculture, and potentially for autonomous vehicles and unmanned air vehicles in the future. In this paper, an extended RTK positioning for significant height differences between two low-cost GNSS receivers with patch antennas is presented. In this case, the classical model for double difference measurements needs to be extended, i.e., a differential tropospheric zenith delay and pseudorange multipath errors need to be estimated besides the baseline and carrier phase integer ambiguities. The increased number of unknowns results in an ill-conditioned system of observation equations and a substantial correlation between the state estimates. We introduce prior information on the differential tropospheric zenith delay based on the differential air pressure and exploit the temporal correlation of pseudorange multipath errors. Moreover, a criterion for the integer ambiguity candidate vector selection is provided, which is robust over phase multipath and unavoidable errors in the float ambiguity covariance matrix. The proposed method is validated with two low-cost GNSS modules that were placed at the Zugspitzplatt (2601 m a.s.l.) and Eibsee (1018 m a.s.l.). The obtained estimates for the baseline and differential tropospheric zenith delay show a high repeatability over several independent ambiguity fixings. The residuals of the fixed carrier phase measurements are within 2 cm for almost all satellites, which confirms both the measurement model and the ambiguity fixing.

show abstract

Research on high‐precision passive localization based on phase difference changing rate

Zhou

Cheng

Lian

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

Summary Passive localization method has a wider applicable prospect as a rising localization resort with well flexibility. The acquisition of phase difference changing rate is a very practical problem in passive localization for the way how to get the phase difference, and a better error precision directly affects the accuracy of the localization. Firstly, based on the analysis of the positioning principle of phase difference changing rate, it studies the characteristics of phase difference changing rate and proposes a novel resolving phase ambiguity algorithm based on a double baselines system cosine theorem (DBSCT), which can eliminate the phase difference ambiguity efficiently. Secondly, it studies two typical methods to extract the high‐precision phase difference changing rate from the unambiguity phase difference, namely, difference algorithm and Kalman algorithm. Finally, it compares the Kalman algorithm with the difference algorithm through computer simulation. Simulation results show that the Kalman algorithm can efficiently smoothen the phase difference data and suppress the measurement noise to achieve a high positioning accuracy. Simulation results also show that with a certain phase difference measurement accuracy, the phase difference rate accuracy extracted by the Kalman algorithm is higher than that we require. Therefore, we can appropriately relax the phase difference measurement accuracy requirements to achieve the same positioning accuracy. So, it has a great application value.

show abstract

Hybrid Cooperative Vehicle Positioning Using Distributed Randomized Sigma Point Belief Propagation on Non-Gaussian Noise Distribution

Cited by 32 publications

References 29 publications

Intelligent Multisensor Cooperative Localization Under Cooperative Redundancy Validation

Intelligent Multisensor Cooperative Localization Under Cooperative Redundancy Validation

Precise Positioning in Alpine Areas with Troposphere and Multipath Estimation

Research on high‐precision passive localization based on phase difference changing rate

Contact Info

Product

Resources

About