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Hybrid Approach-Based Sparse Gaussian Kernel Model for Vehicle State Determination during Outage-Free and Complete-Outage GPS Periods

Vincent Havyarimana,Zhu Xiao,Dong Wang 한국전자통신연구원 2016 ETRI Journal Vol.38 No.3
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  KCI
To improve the ability to determine a vehicle’s movement information even in a challenging environment, a hybrid approach called non-Gaussian square root-unscented particle filtering (nGSR-UPF) is presented. This approach combines a square root-unscented Kalman filter (SR-UKF) and a particle filter (PF) to determinate the vehicle state where measurement noises are taken as a finite Gaussian kernel mixture and are approximated using a sparse Gaussian kernel density estimation method. During an outage-free GPS period, the updated mean and covariance, computed using SR-UKF, are estimated based on a GPS observation update. During a complete GPS outage, nGSR-UPF operates in prediction mode. Indeed, because the inertial sensors used suffer from a large drift in this case, SR-UKF-based importance density is then responsible for shifting the weighted particles toward the high-likelihood regions to improve the accuracy of the vehicle state. The proposed method is compared with some existing estimation methods and the experiment results prove that nGSR-UPF is the most accurate during both outage-free and complete-outage GPS periods.
2
Analytical Study on Inter-Cell Handover via Non-Concentric Circles in Wireless Heterogeneous Small Cell Networks

( Hangyu Gu ),( Shuangchun Li ),( Vincent Havyarimana ),( Dong Wang ),( Zhu Xiao ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.5
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  KISS
In this paper, we propose a novel inter-cell handover approach from a new perspective in dense Heterogeneous and Small Cell Networks (HetSNets). We first devise a cell selection mechanism to choose a proper candidate small cell for the UEs that tend to implement inter-small cell handover (ICH). By exploiting the property of a typical non-concentric circle, i.e., circle of Apollonius, we then propose a novel analytical method for modeling inter-cell handover regions and present mathematical derivation to prove that the inter-small cell handover issues fit the property of the circle of Apollonius. We design an inter-cell handover algorithm (ICHA) by means of our proposed handover model to dynamically configure hysteresis margin and properly implement handover decision in terms of UE’s mobility. Simulation results demonstrate that the proposed ICHA yields lower call drop rate and radio link failure rate than the conventional methods and hence achieve high Handover Performance Indicator (HPI).
3
An Ensemble Cascading Extremely Randomized Trees Framework for Short-Term Traffic Flow Prediction

( Fan Zhang ),( Jing Bai ),( Xiaoyu Li ),( Changxing Pei ),( Vincent Havyarimana ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.4
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Short-term traffic flow prediction plays an important role in intelligent transportation systems (ITS) in areas such as transportation management, traffic control and guidance. For short-term traffic flow regression predictions, the main challenge stems from the non-stationary property of traffic flow data. In this paper, we design an ensemble cascading prediction framework based on extremely randomized trees (extra-trees) using a boosting technique called EET to predict the short-term traffic flow under non-stationary environments. Extra-trees is a tree-based ensemble method. It essentially consists of strongly randomizing both the attribute and cut-point choices while splitting a tree node. This mechanism reduces the variance of the model and is, therefore, more suitable for traffic flow regression prediction in non-stationary environments. Moreover, the extra-trees algorithm uses boosting ensemble technique averaging to improve the predictive accuracy and control overfitting. To the best of our knowledge, this is the first time that extra-trees have been used as fundamental building blocks in boosting committee machines. The proposed approach involves predicting 5 min in advance using real-time traffic flow data in the context of inherently considering temporal and spatial correlations. Experiments demonstrate that the proposed method achieves higher accuracy and lower variance and computational complexity when compared to the existing methods.

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