Constrained Gaussian Condensation Filter for Cooperative Target Tracking

Xu, Chunjing; Wu, Hang; Duan, Shihong

doi:10.1109/jiot.2021.3088297

Cited by 4 publications

(1 citation statement)

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“…Nonetheless, this data-sharing-based approach may cause data incest, which often leads to over-convergence issues. Xu et al [24] proposed a Gaussian condensation filter algorithm based on error-ellipse resampling, which can effectively eliminate accumulated error and outperform particle filtering. Liu et al [25] presented a novel robust cubature Kalman filter to improve data fusion performance in uncertain sensor observation environments.…”

Section: Introductionmentioning

confidence: 99%

An Application-Oriented Method Based on Cooperative Map Matching for Improving Vehicular Positioning Accuracy

Zhou

Chen

et al. 2022

Electronics

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Accurate vehicular positioning is important for intelligent and connected vehicles (ICVs). However, in urban canyons, vehicles that rely solely on global navigation satellite system (GNSS) are susceptible to factors such as signal blocking and multi-path, reducing the positioning performance. In this paper, an application-oriented cooperative map matching (CMM) method is proposed, and a low-cost Global Positioning System (GPS)/BeiDou navigation satellite system (BDS) integrated positioning system is designed. The road constraints of a real traffic environment, which simplifies the computational complexity and facilitates practical applications, are modeled. The positioning system is designed to collect and store the positioning data for experimental analysis. Static and dynamic experiments are conducted to verify the effectiveness of the CMM method. From the experimental results, the mean absolute error (MAE) and root mean square error (RMSE) of the positioning with CMM correction in the static experiment are reduced by 9.0% and 4.9%, respectively. In the dynamic experiment, compared with the original positioning error, the MAE is reduced by 44.2% while the RMSE is reduced by 24.3%. The results show that the proposed method can improve vehicular positioning accuracy effectively in both static and dynamic environments.

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Section: Introductionmentioning

confidence: 99%